Cloud and precipitation processes are still a main source of
uncertainties in numerical weather prediction and climate change
projections. The Priority Programme “Polarimetric Radar Observations meet
Atmospheric Modelling (PROM)”, funded by the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that
many uncertainties relate to the lack of observations suitable to challenge
the representation of cloud and precipitation processes in atmospheric
models. Such observations can, however, at present be provided by the
recently installed dual-polarization C-band weather radar network of the
German national meteorological service in synergy with cloud radars and
other instruments at German supersites and similar national networks
increasingly available worldwide. While polarimetric radars potentially
provide valuable in-cloud information on hydrometeor type, quantity,
and microphysical cloud and precipitation processes, and atmospheric models
employ increasingly complex microphysical modules, considerable knowledge
gaps still exist in the interpretation of the observations and in the
optimal microphysics model process formulations. PROM is a coordinated
interdisciplinary effort to increase the use of polarimetric radar
observations in data assimilation, which requires a thorough evaluation and
improvement of parameterizations of moist processes in atmospheric models.
As an overview article of the inter-journal special issue “Fusion of radar
polarimetry and numerical atmospheric modelling towards an improved
understanding of cloud and precipitation processes”, this article outlines
the knowledge achieved in PROM during the past 2 years and gives
perspectives for the next 4 years.
Introduction and objectives of the priority programme
Among the main sources of uncertainty in the models used in numerical
weather prediction (NWP) and climate change projections are the
parameterizations of cloud and precipitation processes (Bauer et al., 2015).
A major part of these uncertainties can be attributed to missing
observations suitable to challenge the representation of cloud and
precipitation processes employed in atmospheric models. A wealth of new
information on precipitation microphysics and generating processes can be
gained from observations from polarimetric weather radars and their
synergistic analysis at different frequencies. The dual-polarization upgrade
of the United States National Weather Service (NWS) S-band Weather
Surveillance Radar 1988 Doppler (WSR-88D) network was completed in 2013.
Germany finished upgrading its C-band network to polarimetry in 2015 in
parallel with other European countries. The synergistic exploitation of
polarimetric precipitation radars together with measurements from cloud
radars and other instrumentation available at supersites and research
institutions enables a thorough evaluation and potential
improvement of current microphysical parameterizations based on detailed
multi-frequency remote-sensing observations for the first time. Data assimilation merges
observations and models for state estimation as a prerequisite for
prediction and can be seen as a smart interpolation between observations
while exploiting the physical consistency of atmospheric models as
mathematical constraint.
Considerable knowledge gaps still exist, however, in both radar polarimetry
and atmospheric models, which still impede the full exploitation of the
triangle between radar polarimetry, atmospheric models, and data
assimilation and call for a coordinated interdisciplinary effort. The German
Research Foundation (Deutsche Forschungsgemeinschaft, DFG) responded to this
call and established the Priority Programme “Polarimetric Radar Observations
meet Atmospheric Modelling (PROM)”; its first 3-year funding period began
in 2019, which will be followed by a second funding period starting in 2022.
PROM exploits the synergy of polarimetric radar observations and
state-of-the-art atmospheric models to better understand moist processes in
the atmosphere, and to improve their representation in climate and weather
prediction models. The overarching goal is to extend our scientific
understanding at the verges of the three disciplines, radar polarimetry –
atmospheric models – data assimilation, for better predictions of
precipitating cloud systems. To approach this goal the initiators of PROM at
the Universities of Bonn and Leipzig in Germany identified the following
five objectives (see also Trömel et al., 2018):
exploitation of radar polarimetry for quantitative process detection in
precipitating clouds and for model evaluation including a quantitative
analysis of polarimetric fingerprints and microphysical retrievals;
improvement of cloud and precipitation schemes in atmospheric models
based on process fingerprints detectable in polarimetric observations;
monitoring of the energy budget evolution due to phase changes in the
cloudy, precipitating atmosphere for a better understanding of its dynamics;
analysis of precipitation system by assimilation of polarimetric radar
observations into atmospheric models for weather forecasting; and
radar-based detection of the initiation of convection for the improvement
of thunderstorm prediction.
In the first funding period, each of the 14 projects (see
https://www2.meteo.uni-bonn.de/spp2115, last access: 25 October 2021) distributed over Germany contributes
to at least one of these objectives. In most projects, a radar meteorologist
works together with a modeller in order to successfully combine expert
knowledge from both research fields. This overview article of the
ACP–AMT–GMD inter-journal special issue entitled “Fusion of radar
polarimetry and numerical atmospheric modelling towards an improved
understanding of cloud and precipitation processes” outlines methodologies
developed and results achieved from a selection of the projects during the
past 2 years and provides overall perspectives for the next 4 years.
The paper is organized as follows: Sect. 2 explains prevailing challenges
in the representation of clouds in atmospheric models, while Sect. 3
provides methodologies to extend our insight into the microphysics of clouds
and precipitation by exploiting radar polarimetry. Section 4 addresses the
fusion of numerical modelling and radar polarimetry via model evaluation
in radar observation space either using observation operators or using
microphysical retrievals. First conclusions for improved model
parameterizations and for a better representation of model uncertainty in
radar data assimilation are drawn. Section 5 provides a summary and
perspectives for the following years.
Representation of clouds in atmospheric models
The representation of cloud and precipitation processes in atmospheric
models is a central challenge for NWP and climate projections (e.g. Bauer
et al., 2015; Forster et al., 2021), which also impacts offline hydrological
models by modulating the distribution of incoming solar radiation and
precipitation and affecting the simulated hydrological processes such as
evapotranspiration, runoff, and groundwater depths (e.g. Shrestha, 2021).
While the primitive equations provide a solid theoretical basis for
atmospheric model dynamics, the key diabatic processes that drive energetics
and thus circulation are poorly resolved. Important diabatic processes are
linked to cloud and precipitation microphysics acting at scales of
micrometres and turbulent processes ranging from several to hundreds of
metres. While significant progress has been achieved by high-resolution
modelling at the coarser end of this range (e.g. Heinze et al., 2017;
Stevens et al., 2020), the intricate and complex microphysical processes
still require parameterizations in any dynamic atmospheric model down to and
including the scale of direct numerical simulations (e.g. Mellado et al.,
2009).
A key uncertainty in weather prediction and climate modelling results from
the still-rudimentary representation of moist processes and from the
diabatic heating–cooling the models induce due to latent heat and their
interaction with radiation. The generation and interpretation of past and
future climate states additionally has to consider changes in microphysical
processes due to anthropogenic aerosol acting for example as cloud condensation
nuclei and ice-nucleating particles. For short-term weather prediction, the
location and evolution of convective events with lifetimes of hours or less
are particularly challenging, while relatively slowly moving and frontal
systems with lifetimes of days show reasonable predictability (Alifieri et
al., 2012).
Atmospheric modelling in Germany has recently seen substantial advances
in terms of both cloud-resolving simulations in NWP mode and the
implementation of ice and mixed-phase precipitation formation processes.
Traditionally, different model systems were used for NWP and climate
modelling, which were also both heavily used in academic research. The
modelling system for long-term climate integrations is the ECHAM model
(Stevens et al., 2013). Since it was created by modifying global forecast
models developed by ECMWF (European Centre for Medium-Range Weather
Forecasts), its name is a combination of ECMWF and Hamburg, the place of
development of its parameterization package. The COSMO model, however, was
operated at horizontal resolutions down to 2.8 km and used for NWP and
reanalysis studies. Both model families are currently being replaced by the
ICOsahedral Nonhydrostatic (ICON) modelling framework (Zängl et al.,
2015) jointly developed by the Max Planck Institute for Meteorology and the
German national meteorological service (Deutscher Wetterdienst, DWD). Its
climate version (the ICON general circulation model, ICON GCM) inherited its
physics package from the ECHAM model, and the NWP version incorporated the
one from the COSMO model. A third version largely based on the COSMO physics
package was developed for higher resolutions (Dipankar et al., 2015) and
employs a large-eddy turbulence scheme (ICON-LEM). The latter is able to
operate on large domains (Heinze et al., 2017; Stevens et al., 2020) and
includes aerosol–cloud interactions (Costa-Surós et al., 2020). In PROM
primarily the three ICON model variants (ICON-LEM, ICON-NWP, and ICON-A/GCM)
are used.
In most atmospheric models, cloud and precipitation microphysical processes
are represented by bulk microphysical schemes that distinguish between
different hydrometeor classes and include their specific masses as
prognostic variables while their size distributions are parameterized (the
ICON model considered here uses the scheme by Seifert and Beheng, 2006).
Computationally much more demanding are so-called spectral-bin microphysics
schemes (Khain et al., 2015), which evolve cloud and precipitation particle
size distributions discretized into size-interval bins. An example is the
Hebrew University Cloud Model (HUCM) created by Khain et al. (2005) that
treats both liquid and much more intricate (since ice may occur in various
shapes and densities) ice crystal distributions. The model is employed by
some of the PROM projects in addition to the liquid-only bin-microphysics
model by Simmel et al. (2015) extended to the ice phase based on the scheme
by Hashino and Tripoli (2007). For the simulation of the evolution of
specific air volumes, a Lagrangian particle model (McSnow; Brdar and Seifert,
2018) is used in PROM that models ice and mixed-phase microphysical
processes such as depositional growth, aggregation, riming, secondary ice
generation, and melting closer to the real processes than bulk formulations.
Microphysical processes including radiation–particle interactions obviously
depend on particle shape; thus, the evolution of shapes in particle models
– and their signatures in radar observations – is instrumental for a full
understanding and adequate representation of the microphysical processes in
models. Advanced microphysical parameterizations such as spectral-bin or
Lagrangian particle schemes are relevant for cloud-resolving models and
exploited in PROM for the development and improvement of bulk
parameterizations. Scientific questions about global climate require long
model integrations and thus coarse spatial resolutions due to computing time
constraints. At these resolutions (usually of the order of 100×100 km2 in the horizontal), fractional cloudiness needs to be
considered when the grid-box mean relative humidity is below 100 %, which
requires parameterizations of subgrid-scale variability in relative
humidity. Here, PROM builds on assumptions employed in the global ICON model
(ICON GCM) to predict fractional cloudiness (e.g. Quaas, 2012).
Observational insights from polarimetric radar observations and challenges
DWD operates 17 state-of-the-art polarimetric Doppler C-band weather radars
which provide a 3-D sampling of precipitating particles above Germany every
5 min. Together with their Doppler information, radars are the
backbone for precipitation and nowcasting products for all meteorological
services. Although precipitation monitoring is still the most widespread
application of weather radars, their upgrade to polarimetry worldwide not
only improves precipitation estimates, but their observations are also
increasingly exploited for the evaluation and improvement of the
representation of cloud and precipitation processes in atmospheric models
(e.g. Gao et al., 2011; Jung et al., 2012; You et al., 2020; Wang et al.,
2020). Additional observations from cloud radars available at so-called
supersites (in Germany, e.g. the Jülich Observatory for Cloud Evolution
– Core Facility; JOYCE-CF; Löhnert et al., 2015;
http://www.cpex-lab.de, last access: 25 October 2021), universities, and research facilities (e.g. the
Leipzig Aerosol and Cloud Remote Observations System; LACROS; Bühl et
al., 2013) open opportunities to inform and improve atmospheric models. The
use of shorter wavelengths of cloud radars shifts the sensitivity of the
observations towards smaller particles and partly increases the magnitude of
the received polarimetric signals (e.g. KDP – the differential phase
shift between horizontal and vertical polarization per distance called
specific differential phase – scales with λ-1), which allows
for more detailed studies of ice and cloud microphysics. Polarimetric and
multi-frequency radar observations allow for a more granular look at
microphysical processes and provide a great database for model evaluation,
the improvement of microphysical parameterizations, and data assimilation
and thus have the potential to significantly improve both weather forecasts
and climate predictions.
Observations at JOYCE-CF show (a) DWR-KaW, (b)ZDR (measured at
a 30∘ elevation angle), and (c)KDP (also measured at
30∘ elevation angle) on 22 January 2019. Panels (d–f) show the
observed DWR spectrum, ZDR spectrum, and KDP profile at 15:00 UTC;
indicated by the red line in (a–c).
Multi-frequency and spectral polarimetry for ice and cloud microphysics
The PROM project “Understanding Ice Microphysical Processes by combining multi-frequency and spectral Radar polarImetry aNd super-parTicle modelling” (IMPRINT) improves ice microphysical process understanding by using
spectral multi-frequency and radar polarimetric observations in combination
with Monte Carlo Lagrangian super-particle modelling (Brdar and Seifert,
2018). Mid-latitude stratiform clouds, which occur frequently during wintertime over JOYCE-CF, are the main focus. Radar polarimetric variables are
well known to be particularly sensitive to the presence of asymmetric ice
particles (e.g. Kumjian, 2013). Only recently have polarimetric cloud
radars operating at the Ka- or W-band also become routinely available (Oue et al., 2018;
Myagkov et al., 2016; Bühl et al., 2016; Matrosov et al., 2012). Some
polarimetric variables are wavelength dependent (KDP is inversely
proportional to the wavelength), which provides enhanced sensitivity to ice
particle concentration at higher frequencies. Multi-frequency approaches are
complementary to radar polarimetry as they are sensitive to larger ice
particles. Most commonly, the dual wavelength ratio (DWR), defined as the
logarithmic difference of the effective reflectivity Ze at two
frequencies, is used. When ice particles transition from Rayleigh into
non-Rayleigh scattering from one wavelength to a higher one, the DWR
increases, which allows one to infer the characteristic size of the underlying
size distribution. The use of three radar frequencies (e.g. X, Ka, W) extends
the discernable size range; for example, the DWR of the Ka–W combination saturates
for very large particles (Kneifel et al., 2015; Ori et al., 2021). The
information content can be further extended when the Doppler spectral
information is also explored. The different fall velocities allow for the
separation of different hydrometeors; the high differential reflectivity
(ZDR) signal originating from small, slowly falling ice crystals can be
distinguished from the also low ZDR signal of faster falling snow
aggregates, which usually dominate the total ZDR. Only a few studies
so far have used spectral polarimetric observations for ice and snow microphysical
studies (Luke et al., 2021; Oue et al., 2018; Pfitzenmayer et al., 2018;
Spek et al., 2008). The observations collected during the first multi-month
winter campaign carried out at JOYCE-CF as part of the IMPRINT project
provide the opportunity to investigate both polarimetry
and multi-frequency observations in the Doppler spectra space for the first time. An example is
the analysis of the dendritic growth layer (DGL) illustrated in Fig. 1 for a
snowfall event observed on 22 January 2019 at JOYCE-CF. Especially in the
upper half of the cloud, ZDR is enhanced while KDP values are low
(Fig. 1b–c). Starting at the -15 ∘C isotherm, ZDR sharply
decreases and shows an anti-correlation with the enhanced DWR (Fig. 1a) and
KDP values. These polarimetric signatures have been reported by
previous studies (e.g. Moisseev et al., 2015, among others), and the
DWR increase below the -15 ∘C level also resembles the examples shown
in Oue et al. (2018). Oue et al. (2018) concluded, in agreement with findings
in Moisseev et al. (2015), that an increasing concentration of asymmetric
aggregates is partly responsible for enhanced KDP values because the
number of small ice particles decreases due to aggregation. The
spectrally resolved ZDR (sZDR, Fig. 1e), however, reveals that
high-ZDR-producing, slowly falling ice particles are still present down
to the -5 ∘C level. The spectrally resolved DWR (Fig. 1d) shows
that the particles falling from above into the DGL are already partly
aggregated. At -17 ∘C, the spectra are much wider, and a new
spectral mode appears which is linked to the rapid sZDR increase (Fig. 1e). The new ice particle mode increases in Doppler velocity and sDWR until
20 dB is reached. Unlike ZDR, KDP (Fig. 1c and f) remains at
values between 1 and 2 ∘ km-1 down to the -5 ∘C level. A
possible explanation of the bimodal spectra – increased sZDR and
KDP – might be secondary ice processes such as collisional
fragmentation (Field et al., 2017). The few existing laboratory studies
indicate that the number of fragments rapidly increases at -20 ∘C, reaching a maximum at -17 ∘C and decreasing again towards -10 ∘C (Takahashi et al., 1995; Takahashi, 2014). This temperature
dependence fits well to the observed radar signatures in the DGL, although
the laboratory studies only considered collisions of solid ice spheres. As
we can exclude strongly rimed particles in the snowfall case shown in Fig. 1, fragile dendritic structures growing on the surface of aggregates might
be responsible, which precipitate into the DGL and might easily break into
smaller pieces during particle collisions (Fig. 1d). Monte Carlo Lagrangian
super-particle model (Brdar and Seifert, 2018) simulations were recently
extended in IMPRINT by a habit prediction scheme and a parameterization of
ice collisional fragmentation following Phillips et al. (2017). The role of
ice fragmentation and other ice microphysical processes is currently
investigated with a radar observation operator for explaining the observed
radar signatures of intense aggregation shown in Fig. 1.
(a) Dual-wavelength ratio between the C-band POLDIRAD and Ka-band
miraMACS measurements on 7 July 2019, (b) simulated dual-wavelength
ratio, (c) differential radar reflectivity ZDR measured by the C-band
radar POLDIRAD, and (d) simulated ZDR of a comparable, but not
identical, precipitation event using the P3 scheme (Morrison and Milbrandt,
2015).
The PROM project “Investigation of the initiation of convection and the evolution of precipitation using simulations and polarimetric radar observations at C- and Ka-band” (IcePolCKa) combines observations of the C-band Polarization Diversity
Doppler Radar (POLDIRAD) at the German Aerospace Center (DLR),
Oberpfaffenhofen, with those of the Ka-band, Milimeter-wave cloud RAdar of
the Munich Aerosol Cloud Scanner (miraMACS) at
Ludwig-Maximilians-Universität (LMU), Munich. While IMPRINT combines
triple-frequency zenith-pointing observations with spectral cloud radar
polarimetry, IcePolCKa explores the life cycle of convective precipitation
with spatially separated weather and cloud radars in order to quantify ice
crystal properties in precipitation formation. The project focuses on ice
particle growth and its role in precipitation formation within convective
cells. Coordinated range–height indicator (RHI, varying elevation at
constant azimuth) scans along the 23 km long cross section between both
radars allow observation of DWR (Fig. 2a) and ZDR (Fig. 2b) fingerprints of
individual convective cells. While the deviation from Rayleigh scattering
with increasing ice crystal size at the cloud radar wavelength allows one to
distinguish regions dominated by aggregation from regions with
depositional growth, the slanted perspective of the weather radar helps to
narrow down the aspect ratio of ice crystals. Although the DWR technique to
infer ice crystal size is well established (e.g. Kneifel et al., 2015),
assumptions about the unknown ice crystal shape are necessary. Here,
simultaneous polarimetric measurements, like ZDR, help to narrow down
estimates of the average asphericity of ice crystals and reduce ambiguities
in retrieving ice crystal size and ice water content. IcePolCKa develops an
algorithm, which uses ZH, ZDR, and DWR measurements from the two
radars to retrieve ice water content (IWC), the mean particle diameter Dm, and the aspect
ratio of ice crystals using a least-squares fit between measurements and
T-matrix scattering simulations. The model of horizontally aligned spheroids
in combination with an effective medium approximation following Hogan et al
(2012) is used to find the simplest ice particle model which explains the
multi-wavelength polarimetric measurements. The approach allows the study of the
covariance of DWR and ZDR while varying particle density, mean particle
diameter Dm, and aspect ratio. More sophisticated models, such as
discrete dipole approximation (DDA) simulations of specific ice crystals,
would require the knowledge of the aspect ratio, and they make it hard to
identify ice shape collections along these free variables. The
multi-wavelength polarimetric measurements are also used as a benchmark for
convective precipitation formation in NWP models, where cloud microphysics
introduce substantial uncertainty (e.g. Morrison et al., 2020; Xue et al.,
2017). In IcePolCKa, simulated microphysical processes in NWP models are
compared to fingerprints in radar observations: a nested WRF setup covering
the overlap area of both radars is used to simulate convective events with
microphysical schemes of varying complexity while the Cloud-resolving model
Radar SIMulator (CR-SIM; Oue et al., 2020) produces synthetic radar
observations, such as DWR (Fig. 2c) and ZDR (Fig. 2d). Figure 2
illustrates that the Predicted Particle Properties (P3) scheme (Morrison and
Milbrandt, 2015) is able to produce DWR features of similar magnitude and
variability compared to the observations, while a realistic ice particle
asphericity is still missing. IcePolCKa compiled over 30 convective days of
polarimetric measurements and simulations with five different schemes over a
2-year period, which is currently used to analyse how well these different
microphysical schemes reproduce the polarimetric observations. A
cell-tracking algorithm (TINT; Fridlind et al., 2019) facilitates the
comparison on a cell object basis. Comparison of macrophysical cloud
characteristics, such as echo top height or maximum cell reflectivity, shows
that the model simulates too few weak and small-scale convective cells,
independent of the microphysics scheme. In ongoing studies, the P3 scheme
seems to better represent radar signatures within the ice phase, while a
spectral bin scheme tends to better simulate radar signatures within rain,
where all other schemes are not able to correctly reproduce observed
ZDR features.
Measurements of slant-viewing and zenith-pointing polarimetric
C-band weather radar scans with NWP model-based temperature levels and
airborne in situ observations: (a) quasi-vertical profiles (QVPs) of radar
reflectivity ZH, differential reflectivity ZDR, copolar
cross-channel correlation coefficient ρHV, and the specific
differential phase KDP estimated from (noisy) measurements of the
differential phase by aggressive filtering above the melting layer.
(b) Average Doppler spectra from a 15 s birdbath scan and corresponding
first three moments at each radar bin height: reflectivity, power-weighted mean
velocity, and standard deviation. (c) In situ particle images
(downward-looking projection images) collected at altitudes L1 to L9.
The PROM project “A seamless column of the precipitation process from mixed-phase clouds employing data from a polarimetric C-band radar, a micro-rain radar and disdrometers” (HydroColumn) characterizes precipitation processes inside a vertical
atmospheric column by combining polarimetric Doppler weather radar
observations with co-located measurements from micro-rain radars,
disdrometers, and in situ measurements and by relating these observations to
the large-scale atmospheric thermodynamics derived from NWP models. To date,
spectral analyses are mostly performed with cloud radars operating at
shorter wavelengths (see previous paragraphs or, e.g. Shupe et al., 2004;
Verlinde et al., 2013; Kalesse et al., 2016; Gehring et al., 2020; Li and
Moisseev, 2020), but their implementation across the national C-band radar
network offers prospects for operational area-wide applications, e.g. the
identification of dominant precipitation particle growth processes such as
aggregation or riming. While the operational DWD birdbath scan has so far
been used primarily to monitor ZDR (Frech and Hubbert, 2020), HydroColumn now also exploits the Doppler spectra measured at C-band for the analysis
of microphysical process information. Figure 3 shows quasi-vertical profiles
(QVPs; Trömel et al., 2014; Ryzhkov et al., 2016) of polarimetric
variables and Doppler spectra from birdbath scans for a stratiform
precipitation event monitored with the Hohenpeißenberg C-band research
radar (47.8014∘ N, 11.0097∘ E) of DWD together with in situ particle images
obtained by the Falcon research aircraft from DLR during the BLUESKY
campaign (Voigt et al., 2021) within the POLICE project (Sect. 4.2.1). In situ
measurements have been performed with the Cloud, Aerosol and Precipitation
Probe (CAPS; Kleine et al., 2018) integrated in a wing station on the Falcon
flying within a horizontal distance of about 20 km from the radar site and
within about ±15 min of the radar measurements. The dendritic growth
layer (DGL; Ryzhkov and Zrnic, 2019) centred around -15 ∘C is
characterized by ZDR maxima of ∼ 1 dB and KDP of
∼ 0.2 ∘ km-1 and a strong ZH increase towards
lower levels (Fig. 3a). Particle images collected at temperatures below
about -15 ∘C indicate mostly small irregular ice particles with
the number of larger particles increasing toward -15 ∘C (see
levels L1 and L2 in Fig. 3c) and further down also reveal dendrites and
plates (L3, L4). In general, aggregation and riming become highly effective
particle growth mechanisms at temperatures around -7 ∘C
(Libbrecht, 2005), and both processes result in a reduction of ZDR
(Fig. 3a). The vertically pointing Doppler measurements can be used here to
gain a deeper insight into the particle growth process. In this case study,
the Doppler measurements illustrated in Fig. 3b indicate typical
ice-particle fall speeds increasing to about 2 m s-1 just above the
melting layer and thus suggest a transition from predominantly aggregates to
moderately rimed particles based on the relationship between Doppler
velocity and riming degree found by Kneifel and Moisseev (2020). This
conclusion is supported by the corresponding in situ images showing
increasing riming of polycrystals and aggregates toward the melting layer
(L6). The analysis confirms the benefit of interpreting radar signatures
from polarimetric weather radar observations in combination with vertically
pointing Doppler radar measurements, which was previously pointed out for
higher-frequency cloud research radars (Oue et al., 2018; Kumjian et al.,
2020). This novel application of radar spectral analysis to
vertically pointing operational weather radar scans may provide a more
detailed view into intense precipitation events, such as hailstorms, where
the use of cloud radars is severely limited due to the strong attenuation at
high radar frequencies.
Anthropogenic modifications of precipitation microphysics
The PROM project “Polarimetry Influenced by CCN aNd INP in Cyprus and Chile” (PICNICC) seeks to improve our understanding of aerosol effects on
microphysical growth processes in mixed-phase clouds. PICNICC exploits unique
remote-sensing datasets from the LACROS suite (Radenz et al., 2021) extended
with ground-based remote sensing instruments installed at Leipzig
University, Universidad de Magallanes (Punta Arenas), and Cyprus University
of Technology (Limassol). Thus, dual-frequency polarimetric radar
observations from the polluted, aerosol-burdened Northern Hemisphere and from the clean,
pristine Southern Hemisphere can be contrasted for microphysical process
studies as already performed in the project for stratiform mixed-phase
clouds to investigate inter-hemispheric contrasts in the efficiency of
heterogeneous ice formation (Radenz et al., 2021). The PICNICC project
challenges the hypothesis that higher ice crystal concentrations favour
aggregation, which is expected to be more frequent for high aerosol loads and
accordingly higher ice-nucleating particle (INP) concentrations, while
riming should prevail when supercooled liquid layers are sustained due to a
scarcity of INPs. Evaluating this hypothesis requires the distinction between
aggregation and riming in mixed-phase cloud systems. Figure 4 demonstrates for
a deep mixed-phase cloud system passing the low-aerosol site in Punta Arenas
(53∘ S, 71∘ W), Chile, on 30 August 2019, the capability
of the LACROS suite to distinguish between aggregates and rimed particles
when combined with a 94 GHz Doppler radar. The pattern of the 94 GHz radar
reflectivity factor (Ze, Fig. 4a) underlines the complex structure of
the system. The height spectrogram of the vertical-pointing 94 GHz slanted
linear depolarization ratio (SLDR, Fig. 4e) from 08:30 UTC exhibits regions
of changing shape signatures and multi-modality in the cloud radar Doppler
spectra, where multiple hydrometeor populations coexist. The polarizability
ratio ξe (Myagkov et al., 2016; Fig. 4d) obtained from the RHI
scans of SLDR and the co-cross-correlation coefficient of horizontally and
vertically polarized channels in the slanted basis ρs at 35 GHz
(Fig. 4b, c) allows the estimation of a density-weighted hydrometeor shape. SLDR
is more suited for shape classification compared to LDR. By slanting the
polarization basis by 45 ∘, the returned LDR signatures are much less
sensitive to the canting angle distribution of the targets, especially at
low elevation angles (Matrosov et al., 2001; Myagkov et al., 2016). The
polarimetric RHI scans and the Doppler spectra data enable the retrieval of
the vertical profile of the hydrometeors: columnar-shaped bullet rosettes
are formed between 2.5 km height and cloud top as indicated in the RHI scans
by an elevation-constant SLDR (Fig. 4b) and an increase in ρs
with decreasing elevation (Fig. 4c). ξe values around 1.3 (Fig. 4d) are characteristic for slightly columnar crystals. The decreasing
elevation dependence of ρs already at around 3 km height (-15
to -20 ∘C) suggests more random particle orientations; here the
W-band SLDR spectra (Fig. 4e) show reduced values, likely due to the
co-existence of dendritic ice crystals, which are preferably formed in this
temperature range. The co-location of dendrites and columnar crystals can be
explained by either splintering of the arms of the dendritic crystals or a
mixing of locally produced dendrites with columnar crystals from higher up,
or both. Below 2.5 km, ξe decreases toward unity, indicating
the growth of isometric particles. In addition, the vertical-pointing W-band
SLDR slowly decreases toward the cloud base, while fall velocities increase
(Fig. 4e). Both features are characteristic for riming, which is
corroborated by co-located lidar observations that indicate liquid water in
the cloud-base region (not shown). Doppler spectra profiles such as the one
presented in Fig. 4e are also used in a new neural-network-based riming
detection algorithm recently tailored by Vogl et al. (2021) for
vertical-pointing cloud radar observations. This new approach is insensitive
to the mean Doppler velocity, which is – especially at Punta Arenas –
strongly influenced by orographic mountain waves, because the radar
reflectivity factor, skewness, and edge width of the Doppler spectrum are
used instead.
Case study of a deep mixed-phase cloud event observed with
multiwavelength polarimetric cloud radars at Punta Arenas, Chile, on 30
August 2019. (a) Vertical-pointing W-band (94 GHz) radar reflectivity factor
Ze and isolines of modelled air temperature. (b)(c) Ka-band (35 GHz) RHI
scans (90–30∘ elevation) of slanted linear
depolarization ratio (SLDR) and co-cross-correlation coefficient in the
slanted basis ρs, respectively, from 08:30–08:31 UTC. (d) Profile
of the shape index polarizability ratio (ξe) obtained from the RHI
scans shown in (b) and (c) and (e) height spectrogram (at 90∘ elevation) of W-band SLDR from 08:30:00 UTC. The time and height frame of
(b–e) is indicated by the black rectangle in (a).
The PROM project “Investigating the impact of Land-use and land-cover change on Aerosol-Cloud-precipitation interactions using Polarimetric Radar retrievals” (ILACPR) analyses polarimetric radar observations and model simulations
simultaneously in order to improve our understanding of
land–aerosol–cloud–precipitation interactions. The Terrestrial Systems
Modelling Platform (TSMP; Shrestha et al., 2014; Gasper et al., 2014)
developed under the DFG-funded Transregional Research Center TR32 (Simmer et
al., 2015) is used to simulate summertime convective storms passing the
polarimetric X-band radar (BoXPol; e.g. Diederich et al., 2015a, b) located
in Bonn, Germany. TSMP generally underestimates the convective area
fraction, high reflectivities, and width–magnitude of differential
reflectivity (ZDR) columns indicative of updrafts, all leading to an
underestimation of the frequency distribution for high precipitation values
(Shrestha et al., 2021a). A decadal-scale simulation over the region using
the hydrological component of TSMP also shows that much of the variability
in the simulated seasonal cycle of shallow groundwater could be linked to
the distribution of clouds and vegetation (Shrestha, 2021), which further
emphasizes the importance of evaluating the representation of clouds and
precipitation in numerical models. The fusion of radar observations and
models with the aid of observation operators allows for an extended
interrogation of the effects of anthropogenic interventions on precipitation-generating processes and the capabilities of numerical models to reproduce
them. Here, findings from one simulated hailstorm observed on 5 July 2015
passing the city of Bonn, Germany, are explained. Sensitivity simulations
are conducted using large-scale aerosol perturbations and different
land-cover types reflecting actual, reduced, and enhanced human disturbances.
While the differences in modelled precipitation in response to the
prescribed forcing are below 5 %, the micro- and macrophysical pathways
differ, acting as a buffered system to the prescribed forcings (Stevens and
Feingold, 2009; Seifert and Beheng, 2012). Figure 5 shows vertical
cross sections reconstructed from volume scans measured with BoXPol together
with simulated ZH and ZDR for the TSMP simulations with actual
land cover but perturbed condensation nuclei (CN) and ice-nucleating
particle (INP) concentrations. CN concentrations are 100 cm-3 for
maritime and 1700 cm-3 for continental aerosol. Similarly, default INP
concentrations for dust, soot, and organics are 162×103, 15×106,
and 177×106 m-3, respectively. For low (high) INPs, the concentration of soot
and organics are decreased (increased) by 1 order of magnitude. To generate
the synthetic radar observations, the Bonn Polarimetric Radar observation
Operator, B-PRO (Xie et al., 2021, 2016; Heinze et al., 2017;
Shrestha et al., 2021b), is applied. B-PRO is based on the non-polarimetric
version of EMVORADO (Zeng et al., 2016); its code part for computing
unattenuated radar reflectivity on the original model grid (Blahak, 2016)
has been expanded to unattenuated polarimetric variables based on spheroidal
shape assumptions (T-matrix). Because the full polarimetric version of
EMVORADO (Pol-EMVORADO; see Sect. 4.1) was only released very recently,
the model data in ILACPR have been processed using B-PRO. Preliminary
comparisons between B-PRO and Pol-EMVORADO (not shown here) exhibit
negligible differences in their results on the model grid, but Pol-EMVORADO
is much more computationally efficient and takes effects of beam broadening
and attenuation along the actual radar ray paths into account. The vertical
cross sections are compared at different times marked by the vertical grey
bars in the time series of convective area fraction (CAF, Fig. 5a), defined
as the ratio of area with ZH>40 dBZ (at 2 km a.g.l.) to
total storm area. On average BoXPol observations show a bit higher CAF
compared to the simulations. The evolution is always similar in terms of an
initial increase and intensification in the second part of the observation
period, where the experiment with maritime aerosols and low INPs (Mar-lowIn)
is closest to the observations. All simulations show ZH and ZDR
patterns comparable to BoXPol observations; however, the experiment with
continental aerosol and default INPs (Con-defIN, Fig. 5c) shows weaker
ZH while Mar-lowIN (Fig. 5d) shows somewhat higher ZH values
compared to BoXPol (see Fig. 5a). The simulations with maritime CN produce
low cloud droplet concentrations with larger mean diameters compared to the
simulations with continental CN. Accompanied by a very strong updraft, this
also leads to high concentrations of supercooled raindrops above the melting
layer with broader spatial extent (due to a broader updraft region) compared
to the simulations with continental CN and contributes to an enhanced growth
of hail resulting in higher ZH. Also, as shown in the CAF time series,
simulations with continental aerosol and default/high IN tend to exhibit
similar behaviour in radar space, with the latter exhibiting higher CAF only
at latter stages of the storm. The continental CN simulations with default
and high IN differ in terms of simulated updraft speed and total hydrometeor
content, being higher for the latter one. However, Cont-highIN produces
smaller graupel and hail particles compared to Cont-defIN, resulting in
similar ZH. The experiment with continental aerosol and high INP
concentration (Con-highIN, not shown) generates similar polarimetric moments
to Con-defIN. All experiments exhibit vertically extensive columns of
(slightly) enhanced ZDR, collocated with intense simulated updrafts
reaching up to 13 to 14 km. Indeed, ZDR columns emerged as proxies for
updraft strength and ensuing precipitation enhancement (Weissmann et al.,
2014; Simmer et al., 2014; Kumjian et al., 2014; Kuster et al., 2020), and
research on their exploitation for nowcasting and data assimilation is
ongoing. In Fig. 5c, d synthetic ZDR columns are vertically extensive,
while ZDR values within the column stay below 0.3 dB. BoXPol
observations show ZDR columns reaching up to 6 km height only but
with ZDR values exceeding 1 dB. While ZDR values in the lower part
of the columns are mostly generated by large raindrops, freezing drops and
wet hail determine ZDR in the upper parts of the column (Kumjian et
al., 2014; Snyder et al., 2015). The diverging appearance of observed and
synthetic ZDR columns may point to deficiencies in the treatment of
raindrops undergoing freezing and motivates further research. Too rapid
freezing of drops combined with graupel generated from the frozen drops may
generate enhanced but still low ZDR up to high altitudes. Following
Ilotoviz et al. (2018) such attributes of ZDR columns are highly
determined by the vertical velocity, hail size, and aerosol concentration;
e.g. higher CN concentrations lead to higher columns with higher ZDR
values inside and also higher ZH. In this case study and the specific
time step shown, Mar-lowIN (i.e. with lower CN concentration) shows a wider
and somewhat taller ZDR column together with a more intense ZH
core (compare Fig. 5c, d). Further explanations require an improved
representation of the ZDR columns in the model.
Time series of convective area fraction (CAF) evolution (a) and reconstructed observed (b) and simulated/synthetic
range–height indicators (RHIs) of horizontal reflectivity ZH and
differential reflectivity ZDR(c, d). Synthetic RHIs are
based on simulations for actual land cover with different perturbations of
CN and IN concentrations, where Cont-defIN indicates continental aerosol
with default IN concentration, and Mar-lowIN indicates maritime aerosol with
low IN concentration. The gaps in the BoXPol-observed CAF time series are
due to strong attenuation. The vertical grey bars (a) indicate the
times at which the RHIs are compared.
Fusion of radar polarimetry and atmospheric models
Probably the most important and central tool for connecting polarimetric
observations with numerical atmospheric models are observation operators,
which generate virtual observations from the model state. These virtual
observations can be directly compared with the real observations and
signatures of microphysical processes including their temporal evolution.
Thus, the accuracy of precipitation and cloud parameterizations can be
indirectly evaluated, and a database can be established for model optimization.
Missing polarimetric process fingerprints (e.g. Kumjian, 2012) in the
virtual observations may hint at model deficiencies, and model
parameterizations can be adapted in order to increase the coherence between
real and virtual observations. Moreover, sufficiently accurate and fast
observation operators are mandatory for the direct assimilation of
observations using ensemble methods.
However, bulk cloud microphysical parameterizations required for NWP models
include assumptions on several critical parameters and processes which are
not explicitly prognosed or resolved by the governing numerical model. An
example is the inherently assumed particle size distributions and their
relations to the prognostic moments (hydrometeor mass and number densities).
Another challenge is the handling of hydrometeor parameters that are not or
only insufficiently constrained by the model's microphysics but are highly
relevant for the calculation of virtual observations in the (radar)
observation operator. For example, the melting state and shape,
microstructure, and spatial orientation of the different hydrometeors are
not prognostic (or not even implicitly assumed) in most operational bulk
schemes. Therefore, suitable assumptions are required in observation
operators in order to compute meaningful virtual observations. Moreover,
bulk cloud microphysical schemes may only insufficiently approximate the
natural variability, and the interactions between the few assumed
hydrometeor classes and the size distribution moments are mainly tuned to
get, e.g. the surface precipitation right. The current approximations in
both numerical models and observation operators may hence translate into
different sources of errors and biases of the simulated radar variables
(e.g. Schinagl et al., 2019; Shrestha et al., 2021b). As an example, Fig. 7
shows too low polarimetric signals above the melting layer, which are partly
caused by assumptions inherent in the observation operator (see Sect. 4.2.1). Such problems challenge both model evaluation and data assimilation.
Accordingly, central science questions concern the realism of the
sensitivities of simulated radar variables to parameters in the observation
operators and the models as well as effective approaches for the evaluation
and improvement of moist process parameterizations.
Synthetic PPI of ZDR at 0.5 ∘ elevation for the DWD radar
site Neuheilenbach based on the analysis obtained for 4 June at 16:00 UTC by
assimilation of radar reflectivity and using three different ways to specify
the model error: large-scale uncertainty (a), large plus unresolved
scales of uncertainty (b), and in addition the use of the warm bubble
approach (c).
Another challenge for large-scale applications such as long-term model
evaluations or operational real-time data assimilation based on large radar
networks is the high computational demand and low speed of current
polarimetric radar observation operators. Often, the operators apply some
kind of pre-calculated lookup tables (LUTs) of scattering properties and
parallelization techniques for speed optimizations (e.g. Wolfensberger and
Berne, 2018; Matsui et al., 2019; Oue et al., 2020). Despite that, radar
simulations for a single time step take – depending on the computer – on the
order of minutes for one single plan position indicator (PPI) scan
(Wolfensberger and Berne, 2018) or for a single model scene (CR-SIM; Oue et
al., 2020). Matsui et al. (2019) state the LUT generation process of their
POLARRIS operator to only take a few minutes when distributed to a few
thousand processors, but they do not elaborate on the required times for the
actual simulation of the radar measurement. The operator B-PRO (Xie et al.,
2016), which uses neither of these techniques, is much slower, as
applications within SPP-PROM have demonstrated (Shresta et al., 2021b).
While acceptable for research, real-time operational applications may pose
much stricter time constraints. Therefore, an important technical goal is to
provide an efficient, yet physically accurate and “state-of-the-art”,
polarimetric radar operator to the community, which reduces the simulation
time for multi-elevation PPI scans of many stations to a few seconds.
Quasi-vertical profiles (QVPs) of observed (left column) and
simulated (right column) polarimetric radar variables' horizontal
reflectivity ZH(a, b), differential reflectivity ZDR(c, d), and specific differential phase KDP(e, f)
together with radar-retrieved (g) and simulated ice water content
(IWC, h). The QVPs show a stratiform rain event observed on 7 October
2014 between 00:00 and 03:30 UTC with the polarimetric X-band radar in Bonn,
BoXPol, and simulated with COSMO version 5.1 and the two-moment cloud
microphysics scheme.
Polarimetric radar observation operator development
Within the PROM project Operation Hydrometeors, the up-to-now non-polarimetric radar observation
operator EMVORADO (Zeng et al., 2016; Blahak and de Lozar, 2020; Blahak,
2016) has been extended to polarimetry (Mendrok et al., 2021).
(Non-polarimetric) EMVORADO has been designed to efficiently simulate PPI
volume scan measurements of entire radar networks from the prognostic model
state of an NWP model for direct comparisons with the radar observations.
EMVORADO is part of the executable of both the COSMO and ICON NWP models,
which allows us to run the operator within a NWP model run and to access the
model state and radar variables in memory. The code is MPI- and
OpenMP-parallelized and thus fully exploits the computational power of
modern high-performance computers (HPCs) and avoids storing and re-reading extensive model state data
to and from hard drives. This enables large-scale real-time applications such as
operational data assimilation and extensive NWP model verifications using
whole radar networks at high temporal resolution. Its modular nature allows
for relatively easy interface development to other NWP models. An offline
framework is also available, which accesses model states of one model time
step from hard disc. EMVORADO includes detailed modular schemes to simulate
beam bending, beam broadening, and melting effects and allows users to
choose for each process between computationally cheap and physically
accurate options. The operator has been used for the assimilation of radar
reflectivity with a positive impact on precipitation forecasts (Bick et al.,
2016; Zeng et al., 2018, 2019, 2020). Currently, DWD uses EMVORADO to
operationally assimilate 3D volumetric reflectivity and radial wind
observations of its C-band radar network. Key for this application is also
the extensive use of precomputed lookup tables that relate (Mie-theory-based) bulk reflectivity directly to hydrometeor densities and temperature.
The effects of neglecting radar beam pattern and broadening and of
hydrometeor fall speeds on data assimilation have been investigated in a
joint effort together with the PROM project “Representing model error and observation Error uncertainty for Data assimilation of POLarimetric radar measurements” (REDPOL) (Zeng et al., 2021a).
The polarimetry-extended EMVORADO, in the following referred to as
Pol-EMVORADO, has inherited all features of EMVORADO, which in turn have
been expanded where necessary to calculate and handle polarimetric
variables. This includes, e.g. beam bending, beam broadening, and beam
smoothing schemes; effective medium approximations allowing one- and two-layered
hydrometeors with different water–ice–air mixing schemes and melting
topologies; and a lookup table approach for efficient access to
polarimetric observables such as ZDR, LDR, ρHV, and
KDP. Optionally, attenuation effects can be considered, specific and
differential attenuation (AH and ADP, respectively) can be provided, and
further output quantities derivable from the complex scattering amplitudes can be
easily added. Pol-EMVORADO applies state-of-the-art scattering properties of
spheroidal particles derived by one-layered (Mishchenko, 2000) and
two-layered T-matrix approaches (Ryzhkov et al., 2011). Assumptions on
spheroid shape and orientation follow parameterizations introduced in
Ryzhkov et al. (2011). The lookup table approach has been revised to
accommodate additional parameters necessary to derive the full set of
polarimetric radar output. For a given set of parameters affecting the
hydrometeor scattering properties, the lookup tables are created only once,
stored in files, and re-used for subsequent runs.
Using pre-existing lookup tables, the computations for virtual polarimetric
volume scans of radar networks are very fast. For example, simulating the
volume scan observations of all polarimetric parameters for all 17 German
radars takes only a few seconds on a Linux workstation (8 cores) and adds
only about 1 s per radar output time step to the model runtime when
performed online during a run of ICON-D2 (DWD's operational
convection-allowing ICON version with 2 km grid spacing) on DWD's NEC Aurora
supercomputer. That is, simulating polarimetric radar data in intervals of 5 min as observed by DWD's weather radar network adds only a few percent of
the total model runtime (Mendrok et al., 2021), enabling the exploitation of
Pol-EMVORADO for the assimilation of high-temporal-resolution polarimetric
radar data in an operational framework. Pol-EMVORADO has been incorporated
into the official version of EMVORADO and can be run online (i.e. within a
COSMO or ICON run) as well as offline (i.e. stand-alone with model fields
from data files). Although designed as a PPI volume scan observation
operator for a radar network, its output can also be provided on NWP model
grids. An example of a ZDR volume scan simulated by Pol-EMVORADO for
the REDPOL project is shown in Fig. 6 (see also Sect. 4.2.3).
In summary, (Pol-)EMVORADO comprises a wide set of state-of-the-art
features. While each of these features is also provided by other observation
operators (Pol-)EMVORADO is, to our knowledge, unique in combining them
into one operator that allows the simulation of virtual observations, including
instrumental effects and in formats directly comparable to real
observational scans, from NWP model runs in a comparably accurate and very
fast manner targeted at operational applications. Mendrok et al. (2021) give
a comprehensive description of the features developed or updated for
Pol-EMVORADO including details on their implementation and performance.
However, from the application of Pol-EMVORADO (or B-PRO; see Sect. 3.2)
within PROM, a number of problems became evident. Modelling hydrometeors as
homogeneous effective-medium particles (e.g. oblate spheroids) does not
reproduce the polarimetric signatures of low-density hydrometeors like
dendrites or aggregates typical for snow while keeping their microphysical
properties well (e.g. aspect ratio, degree of orientation) within realistic –
observed or model-predicted – ranges and consistent between different radar
frequencies. This deficiency has been demonstrated and explained from
electromagnetic theory by Schrom et al. (2018). It is obvious in one case
study (Shrestha et al., 2021b) and in Fig. 7, where ZDR and KDP in
the snow-dominated layer between 2.5 and 5 km height almost entirely lack
the typical observed features, i.e. bands of enhanced ZDR and KDP
in the dendritic growth layer that then smoothly decrease to mostly
positive, non-zero values towards the melting layer. This deficiency can
also be observed with other polarimetric observation operators applying a
T-matrix approach (see simulation-to-observation comparisons in
Wolfensberger and Berne (2018), Matsui et al. (2019), and Oue et al. (2020),
where the lack of ZDR and KDP signatures is not discussed at all
or exclusively explained by a lack of secondary ice, though), which
nevertheless currently constitutes the state of the art in radar
polarimetry. Orientation and shape of frozen and melting hydrometeors are
very variable, both in nature and in the assumptions used in observation
operators, which translates into large uncertainties in polarimetric radar
signatures (e.g. Matsui et al., 2019; Shrestha et al., 2021b).
To tackle these challenges, it is planned to interface Pol-EMVORADO to
scattering databases or other scattering models in order to enable more
realistic cloud ice and aggregate snowflake scattering properties and allow
for improvements or extensions of the polarimetry-related microphysical
assumptions (shape/habit/microstructure, orientation, and their distribution,
e.g. Wolfensberger et al., 2018), particularly for (partly) frozen
hydrometeors. For PROM's second phase, we have proposed taking this up
guided with Lagrangian particle model information as well as testing the
application of Pol-EMVORADO in an operational data assimilation environment.
Retrieved and simulated graupel mixing ratios, defined as the
percentage of graupel in the total hydrometeor mass, for the stratiform rain
event shown in Fig. 7 (7 October 2014, 00:00–03:30 UTC). An advanced
hydrometeor classification and quantification algorithm has been applied to
polarimetric BoXPol measurement (a) and to simulated radar variables
based on COSMO simulations (c) and compared to the COSMO-simulated
graupel mixing (b).
Model evaluation and improvements using forward simulations and
microphysical retrievalsConvection-resolving simulations with COSMO
In a joint effort, the PROM projects Operation Hydrometeors and ILACPR evaluate simulated stratiform
precipitation events in radar observation space and develop a sophisticated
polarimetry-based hydrometeor classification and quantification for the
evaluation of the representation of hydrometeors in numerical models. Based
on a stratiform event monitored on 7 October 2014 with the Bonn polarimetric
X-band radar BoXPol, Fig. 7 illustrates the potential of using polarimetric
observations for the evaluation and improvement of microphysical
parameterizations. Figure 7a–f compare QVPs of measured and virtual ZH,
ZDR, and KDP with the Bonn Polarimetric Radar observation
Operator B-PRO (Xie et al., 2021) to forecasts simulated with COSMO version
5.1 using its two-moment cloud microphysics scheme (itype_gscp = 2683; Seifert and Beheng, 2016). Due to a small spatial shift of the
precipitation event in the simulations, the observations at 50.7305∘ N,
7.0717∘ E are compared with simulations at a close-by grid point at 51.1∘ N,
7.0717∘ E. As demonstrated in Shrestha et al. (2021b) using a similar
stratiform precipitation event, COSMO tends to simulate considerable amounts
of melting graupel partly reaching the surface, which results in higher
synthetic ZDR than observed (compare Fig. 7c, d) within and below the
melting layer (ML). Above the ML, however, synthetic ZDR already
approaches 0 dB at around 6 km height, which indicates deficiencies in the
ice–snow partitioning in COSMO as well as in the assumed snow morphology
(soft spheroids) in the observation operator, both resulting in too low
polarimetric signals. While the observed and simulated ZH is comparable
in terms of structure and magnitude – except a more pronounced observed ML –
larger differences exist with respect to KDP above the ML (Fig. 7e, f).
While observations show bands of enhanced KDP within the dendritic
growth layer (DGL) centred around -15 ∘C, the simulated KDP
is very weak, indicating a lower concentration of crystals and early
aggregates compared to observations (e.g. Moisseev et al., 2015). Ice water
content (IWC) above the ML retrieved from measured KDP and differential
reflectivity in linear-scale Zdr, i.e. IWC(KDP, Zdr)
following Ryzhkov et al. (2018), agrees well with IWC modelled by COSMO in
terms of structure but has lower magnitudes (compare Fig. 7g, h) in line
with the lower simulated KDP. Overall, Fig. 7 supports the hypothesis
of a too strong graupel production in the simulations. Operation Hydrometeors also developed a
robust radar-based hydrometeor classification (HMC) and mixing ratio
quantification algorithm following Grazioli et al. (2015) and Besic et al. (2016, 2018) for the evaluation of the representation of hydrometeors in NWP
models (standard output is the dominant hydrometeor type only). This HMC is
based on clustering and has the advantage that the radar data are separated
into clusters based on their polarimetric similarity (no theoretical
preliminary calculation is needed), which are then identified as hydrometeor
classes. Various clustering methods can be used here (e.g. Lukach et al., 2021). The new method is relatively insensitive to uncertainties in the
scattering properties of ice particles. Its application to the BoXPol
observations does not indicate graupel below the ML (Fig. 8a), while COSMO
simulates a pronounced thick graupel layer (Fig. 8b), including some melting
graupel particles reaching the ground around 01:45 UTC. Applying the HMC
to the virtual observations, however, does not reproduce a graupel layer of
similar intensity (Fig. 8c), probably caused by a too strong ZH and
temperature influence (compare with Fig. 7) relative to the polarimetric
variables in the classification scheme which needs further investigation. A
persistent challenge in according routines is that clusters are always
separated by the 0 ∘C level (e.g. Ribaud et al., 2019); i.e. hail or
graupel are identified as clusters only below or above the melting layer.
For the case study in Shrestha et al. (2021b) the simulated graupel layer
was even more pronounced and sensitivity experiments were performed to guide
model improvement: increasing the minimum critical particle diameter
Dcrit, which is required for self-collection of ice particles
(aggregation), increased/improved the ice–snow partitioning, and a lower
temperature threshold for snow and ice riming, Trime, considerably
reduced the graupel production.
Comparing state-of-the-art polarimetric retrievals of liquid water content
(LWC), ice water content (IWC), particle number concentration Nt, and
mean particle diameter Dm (e.g. Ryzhkov et al., 2018; Ryzhkov and
Zrnic, 2019; Bukovčić et al., 2020; Reimann et al., 2021; Trömel
et al., 2019) with their simulated counterparts can also be used for
evaluating NWP models and for data assimilation (Carlin et al., 2016). Figure 7g, h show higher IWC(KDP, Zdr) than simulated by COSMO for
the case study discussed earlier. However, for more solid conclusions about
possible model errors, as well as for the use of retrieved quantities for
data assimilation, the retrieval uncertainties must be estimated. The
analysis of data collected in the ice regions of tropical convective clouds
indicate that IWC(KDP, Zdr) yields a root-mean-square error
of 0.49 g m-3 with the bias within 6 % (Nguyen et al., 2019).
Murphy et al. (2020) introduced the columnar vertical profile (CVP)
methodology to follow the track of research aircraft and better co-locate
in situ data to radar microphysical retrievals. Applying the methodology to
two mesoscale convective systems, they found the best performance of
polarimetric microphysical retrievals in regions of high ZDR and high
KDP but recommend a much larger dataset to finally conclude on the
accuracy of these retrievals.
Specific ice water, qi (g kg-1), as zonal, annual mean for
(top) standard ICON GCM output, (middle) aggregation parameterization
revised as stochastic parameterization drawing from the qi
subgrid-variability PDF, and (bottom) difference between the two.
The PROM project “POLarimetric signatures of ICE microphysical processes and their interpretation using in situ observations and cloud modelling” (POLICE) evaluates radar retrievals and models using in particular
in situ observations of microphysical cloud parameters from the research
aircraft HALO (e.g. Wendisch et al., 2016; Voigt et al., 2017) and Falcon
(e.g. Voigt et al., 2010, 2014; Flamant et al., 2017).
Currently, ground-based polarimetric radar measurements and aircraft in situ
data from the Olympic Mountain Experiment (OLYMPEX; Houze et al., 2017;
Heymsfield et al., 2018) are exploited to investigate riming processes and
to evaluate retrievals of ice water content (IWC), particle number
concentration Nt, and mean particle diameter Dm (e.g. Ryzhkov et
al., 2018; Ryzhkov and Zrnic, 2019; Bukovčić et al., 2020; Carlin et
al. 2021). The OLYMPEX mission took place on the Olympic Peninsula of
Washington State (USA) from November 2015 through February 2016. University
of North Dakota's (UND) Cessna Citation II equipped with an in situ cloud
payload overpassed the National Science Foundation (NSF) Doppler On Wheels
(DOW, mobile polarimetric X-band radar with about 60 km range and 74 m
radial resolution), placed in the Chehalis Valley at Lake Quinault (47.48∘ N,
123.86∘ W, 64 m altitude) performing RHI scans within an azimuthal sector of
22 ∘. Measurements and microphysical retrievals of the DOW and the
Citation, respectively, are currently evaluated and will then be compared at
matched space-time coordinates for several flight transects.
Climate simulations with ICON-GCM
A major part of the uncertainties in representing clouds and precipitation
in atmospheric models can be attributed to unresolved variability that
affects resolved variables via non-linear processes. Current climate model
horizontal resolutions are on the order of 100 km. But even for NWP models,
which have resolutions between 10 km for global and 1 km for regional
simulations, most cloud processes remain unresolved. The project Climate model PArameterizations informed by RAdar (PARA) evaluates
and improves the representation of cloud and precipitation processes in
particular for climate models and focuses on precipitation formation in ice
clouds. Since most surface precipitation over continents and extra-tropical
oceans involves the ice phase (Mülmenstädt et al., 2015; Field and
Heymsfield, 2015), its reliable representation is paramount and thus the
focus of PARA. Microphysical parameterizations typically consider only the mean
cloud liquid or ice water content to compute process rates, which causes
biases in all nonlinear processes including radiation (e.g. Cahalan, 1994;
Carlin et al., 2002) and precipitation formation (e.g. Pincus and Klein,
2000). Realistic results thus require the tuning of process rates (e.g.
Rotstayn, 2000) or realistic estimates of subgrid-scale cloud variability
and its inclusion in the process parameterizations. To tackle this issue,
PARA exploits inherent model assumptions for treating fractional cloudiness.
Since the early works of Sommeria and Deardorff (1977), atmospheric models
assume or predict some notion of subgrid-scale variability of relative
humidity. Some models do so by predicting cloud fraction (e.g. Tiedtke,
1993), and others use a diagnostic representation of the subgrid-scale
probability density function (PDF) of total water specific humidity, qt
(e.g. Sundqvist et al., 1989; Smith, 1990; Le Treut and Li, 1991; Rosch et
al., 2015). Another option is to utilize a prognostic probability density
function (PDF) of qt by assuming a functional form and predicting the
shape parameters of the PDF (e.g. Tompkins, 2002; Neggers, 2009). The
German climate and weather prediction model ICON in its version dedicated to
climate simulations (general circulation model version, ICON-GCM) inherits
the representation of physical processes from its predecessor ECHAM6
(Stevens et al., 2013) and uses the Sundqvist et al. (1989) parameterization
for a diagnostic PDF of the total-water specific humidity, qt.
As a first step, PARA analyses the implied PDF of cloud ice using satellite
observations from combined CloudSat-CALIPSO radar–lidar satellite
observations (DARDAR; Delanoë et al., 2014). Interestingly, a first
direct comparison of IWC profiles obtained from DARDAR with polarimetric
retrievals based on the ground-based BoXPol radar shows an overall good
agreement, except for columns with an integrated ice water path integrated water path (IWP)
> 1 kg m-2. In these regions pronounced polarimetric
signatures result in high IWC at higher altitudes, which are not
reproduced by reflectivity-only retrievals or the DARDAR retrievals. The
statistics are currently evaluated on a larger database, which is also used
to investigate the impact on the parameterizations in ICON-GCM. In the
second step, a stochastic parameterization approach is taken to allow for an
unbiased computation of cloud microphysical process rates on average. Based
on the cumulative distribution function (CDF), a random number generator
draws from the CDF according to the simulated likelihood a plausible value
of the specific ice mass based on which the microphysical process is
computed. This specifically considers the formation of solid precipitation
(snow) from ice clouds via aggregation and accretion processes (Lohmann and
Roeckner, 1996; Stevens et al., 2013), and subsequently the evaporation of
precipitation below the clouds. The result of the revised aggregation
parameterization is shown in Fig. 9. The increased aggregation rate, which
is a linear function of the specific cloud ice, qi, leads to an average
decrease in qi. The aggregation rate is directly linked to the
accretion rate, which lowers the effect of qi decrease. An
investigation of the influence of the revised aggregation parameterization
on the different microphysical process rates – which are related to the ice
phase – is currently performed. A detailed evaluation of the new versus old
parameterization with the ground-based polarimetric radar is on its way and
will in particular focus on the timescales of evaporation of precipitation
below the cloud.
Data assimilation
Within an idealized framework, Jung et al. (2008, 2010) and Zhu et al. (2020) demonstrated benefits of assimilating simulated polarimetric data for
the estimation of microphysical state variables. Up to now, however, direct
assimilation of real polarimetric data poses great challenges due to the
deficiencies of cloud and precipitation schemes in NWP models in
realistically representing and providing the necessary information
(optimally the distribution of particle size, shape, and orientations in all
model grid boxes) required by a polarimetric radar observation operator and
therefore causing large representation error (Janjic et al., 2018). Both the
specification of model error to examine uncertainty in microphysics (Feng et
al., 2021) and the specification of the observation error for polarimetric
radar observations that include estimates of the representation error (Zeng
et al., 2021b) are investigated in the PROM project REDPOL. For the assimilation
of radar reflectivity with an ensemble Kalman filter, several approaches for
including model errors during data assimilation are explored, including (1) additive noise with samples representing large-scale uncertainty (see Zeng
et al., 2018), (2) combination of large-scale and unresolved-scale uncertainty
(Zeng et al., 2019), and finally (3) adding to these warm bubble triggering
of convective storms in case they are missing in the 1 h forecast but
present in corresponding observations (Zeng et al., 2020). Applying
Pol-EMVORADO to the analysis obtained by assimilating radar reflectivity
from the German C-band network, Fig. 6 illustrates the resulting
differences of these three techniques in ZDR space. Obviously,
synthetic ZDR values depend on the strategy used to specify the model
error, putting another weight to the argument that assimilation of radar
reflectivity alone is not sufficient to constrain the estimation of
microphysical state variables and that polarimetric information is also required. First results in this direction were reported by Putnam et al. (2019), who assimilated ZDR below the melting layer but reported
problems with the assimilation of KDP data for a supercell case due
to high observation errors as a result of contamination from wet hail, dust,
and debris and nonuniform beam filling.
Summary and perspectives
The Priority Programme Polarimetric Radar Observations meet Atmospheric Modelling (PROM) (SPP 2115, https://www2.meteo.uni-bonn.de/spp2115/, last access: 25 October 2021)
was established in April 2017 by the Senate of the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) and is designed to
run for 6 years. PROM is a coordinated effort to foster partnerships
between cloud modellers and radar meteorologists and thus to accelerate the
exploitation of polarimetric weather radars to improve the representation of
cloud and precipitation processes in numerical models. The first funding
phase engaged in an as-complete-as-possible exploitation and understanding
of nation-wide polarimetric measurements complemented by state-of-the-art
measurement devices and techniques available at supersites. Bulk
polarimetric measurements available over Germany are complemented with
multi-frequency observations and spectral polarimetry for detailed studies
of ice and cloud microphysics. Thus, modellers now hold an unprecedented
amount of three-dimensional microphysics-related observational data in their
hands to improve parameterizations. Key tools for the fusion of radar
polarimetry and atmospheric modelling, e.g. the Monte Carlo Lagrangian
particle model McSnow and the polarimetric observation operator
Pol-EMVORADO, have been developed. PROM started with detailed investigations
of the representation of cloud and precipitation processes in the COSMO and
ICON atmospheric models exploiting polarimetric observation operators. First
improvements of the two-moment cloud and precipitation microphysics scheme
are made and more are expected in phase 2. In addition, intercomparisons of
microphysics schemes in radar space have been performed. Phase 1 further
developed microphysical retrievals, determined their uncertainties, and
started their exploitation for model evaluation and radar-informed
parameterizations. The developed prerequisites pave the way to finally
exploit polarimetry for indirect and direct data assimilation in the
upcoming second funding phase.
Some tools developed in phase 1, however, still require refinement in phase
2. The T-matrix calculations for electromagnetic scattering by spheroidal
particles represent only a crude approximation to frozen and mixed-phase
hydrometeors, especially for pristine ice particles and aggregate snowflakes
at cloud radar wavelengths. It is not possible to reproduce observed
polarimetric signatures of snow with the T-matrix approach (i.e. homogeneous
ice–air spheroids) and realistic microphysics (shape, orientation).
Refinements include interfacing to a new scattering database based on discrete dipole approximation
(DDA) for realistic ice and snow particles for
all relevant weather radar wavelengths and improvements of the melting
scheme of graupel and hail.
Based on the progress made, the fusion of radar polarimetry and atmospheric
modelling can be approached even more aggressively in phase 2. While
objective 1 received most attention in phase 1, more projects will exploit
the observational insights and tools developed to finally improve
parameterizations and assimilate polarimetric information; i.e. more
emphasis will be put on objectives 2 and 4 in phase 2. Direct assimilation
of polarimetric variables remains challenging, because NWP models need to
realistically represent and provide the necessary information required by a
polarimetric radar observation operator; ideally the distribution of
particle size, shape, and orientation would be required in all model grid
boxes. Indirect assimilation of polarimetric information (e.g. microphysical
retrievals and process signatures), however, is less demanding to the model
and should be pursued in parallel. Modern Bayesian data assimilation
techniques are sensitive to both model and observation operator biases, so
that further work on these issues is of great importance for a successful
data assimilation.
Data availability
The data presented in this paper are available through the authors upon
request. Polarimetric radar data from the operational C-band radar network
are also available from the German Weather Service (DWD). Specific campaign
data will be published in addition.
Author contributions
ST had the initial idea and mainly organized and structured
the joint publication. ST, JQ, and CS
formed the editorial team consolidating the text. All authors contributed to
specific sections of the paper and commented on the paper.
Competing interests
Some authors are members of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors have also no other competing interests to declare.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes (ACP/AMT/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We gratefully acknowledge the funding of the German Research Foundation
(DFG) to initialize the special priority programme on the Fusion of Radar
Polarimetry and Atmospheric Modelling (SPP-2115, PROM). The work of
contributing authors was carried out in the framework of the projects
Operation Hydrometeors (grants TR 1023/16-1 and BL 945/2-1), IcePolCKa (HA
3314/9-1 and ZI 1132/5-1), ILACPR (grant SH 1326/1-1), IMPRINT (grant KN
1112/3-1), POLICE (grants TR 1023/13-1 and VO 1504/5-1), PARA (grants QU
311/21-1 and TR 1023/15-1), HydroColumn (grant FR 4119/1-1), REDPOL (grant
JA 1077/5-1), and PICNICC (grants KA 4162/2-1 and SE 2464/1-1). ILACPR
gratefully acknowledges the computing time (project HBN33) granted by the
John von Neumann Institute for Computing (NIC) and provided on the supercomputer JUWELS at Jülich Supercomputing Centre
(JSC).
Financial support
This research has been supported by the Deutsche Forschungsgemeinschaft (grant nos. TR 1023/16-1, BL 945/2-1, SH 1326/1-1, TR 1023/13-1, VO 1504/5-1, QU 311/21-1, TR 1023/15-1, FR 4119/1-1, JA 1077/5-1, KA 4162/2-1, SE 2464/1-1, KN 1112/3-1, ZI 1132/5-1, and HA 3314/9-1).
Review statement
This paper was edited by Timothy Garrett and reviewed by two anonymous referees.
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