Aerosol–cloud interactions (ACIs) have been widely recognized as a factor
affecting precipitation. However, they have not been considered in the
operational National Centers for Environmental Predictions Global Forecast
System model. We evaluated the potential impact of neglecting ACI on the
operational rainfall forecast using ground-based and satellite observations
and model reanalysis. The Climate Prediction Center unified gauge-based
precipitation analysis and the Modern-Era Retrospective analysis for Research
and Applications Version 2 aerosol reanalysis were used to evaluate the
forecast in three countries for the year 2015. The overestimation of light
rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and
65.74 % for moderate rain, heavy rain, and very heavy rain, respectively)
from the model are qualitatively consistent with the potential errors arising
from not accounting for ACI, although other factors cannot be totally ruled
out. The standard deviation of the forecast bias was significantly correlated
with aerosol optical depth in Australia, the US, and China. To gain further
insight, we chose the province of Fujian in China to pursue a more insightful
investigation using a suite of variables from gauge-based observations of
precipitation, visibility, water vapor, convective available potential energy
(CAPE), and satellite datasets. Similar forecast biases were found:
over-forecasted light rain and under-forecasted heavy rain. Long-term
analyses revealed an increasing trend in heavy rain in summer and a
decreasing trend in light rain in other seasons, accompanied by a decreasing
trend in visibility, no trend in water vapor, and a slight increasing trend
in summertime CAPE. More aerosols decreased cloud effective radii for cases
where the liquid water path was greater than 100 g m
Aerosols affect precipitation by acting as cloud condensation nuclei (CCN) and ice nuclei (IN), which can influence cloud microphysics (Twomey et al., 1984) and cloud lifetime (Albrecht, 1989). By absorbing and scattering radiation in the atmosphere, aerosols can alter the thermal and dynamic conditions of the atmosphere. The two types of effects are broadly referred to as aerosol–cloud interactions (ACIs) and aerosol–radiation interactions (ARIs) (Intergovernmental Panel on Climate Change, 2013). Both can influence precipitation (Rosenfeld et al., 2008) and many other meteorological variables to the extent that they may account for the considerable changes in climate experienced in Asia over the past half century (Li et al., 2016).
The impact of aerosols on precipitation via cloud microphysics occurs through
warm-rain and cold-rain processes, as reviewed by Tao et al. (2012). In the
warm-rain process, the competition for water vapor leads to a greater number
of cloud drops with smaller sizes as the aerosol loading increases. This
decreases the collision efficiency because of the low fall speed and low
droplet-collecting efficiency. Rain formation is thus slowed down. Also, a
heavier aerosol loading narrows the cloud drop-size spectrum, lowering the
coalescence and collision efficiencies. The delay in precipitation formation
from the warm-rain process enhances condensation and freezing and, ultimately,
leads to the release of extra latent heat above the 0
Most findings concerning the aerosol suppression of clouds and precipitation are associated with stratocumulus clouds, cumulus clouds, and shallow convection (Albrecht, 1989; Rosenfeld, 2000; Jiang et al., 2006; Xue and Feingold, 2006; Khain et al., 2008), whereas those of enhanced rainfall are associated with deep convective clouds (Koren et al., 2005; Lin et al., 2006; Bell et al., 2008; Rosenfeld et al., 2008). Li et al. (2011) used 10 years of ground-based observations to examine the long-term impact of aerosols on precipitation and found rainfall enhancement in mixed-phase warm-base clouds and suppression in liquid clouds. Van den Heever et al. (2011) underlined the importance of cloud type in dealing with the impact of aerosols on precipitation.
Forecasting rainfall is most challenging and important in numerical weather prediction (NWP). In the current Global Forecast System (GFS) model, aerosols are only considered in the radiation scheme on a climatological scale. ARIs are only considered off-line and are not coupled with the dynamic system. ACIs have not yet been accounted for. To improve the forecast accuracy, a suite of new physical schemes are being implemented in the National Centers for Environmental Prediction (NCEP)'s Next Generation Global Prediction System. The goal of modifying the current forecast model is to improve physical parameterizations in a way that allows for efficient, accurate, and more complete representations of physical processes and their interactions including at least some of the aforementioned aerosol mechanisms.
As a first step, the goal of the present study is to evaluate current operational GFS forecast results (before any ACIs are introduced) to see if any systematic precipitation biases bear resemblance to aerosol perturbations. A gross evaluation of the GFS model forecast results in three countries (China, the US, and Australia) being chosen because they cover all hemispheres and represent different atmospheric and environmental conditions. Moreover, there are the US Department of Energy's Atmospheric Radiation Measurement (ARM) observations in all three countries that will be used in follow-on studies to gain a deeper insight into causal relationships and the impact of different parameterization schemes. Descriptions of the operational GFS model, datasets, and the evaluation strategy and statistical method used are presented in Sect. 2. Results of the evaluation and possible explanations are given in Sect. 3. A summary of the research and discussion are given in Sect. 4.
The NCEP GFS model is a global spectral forecast model (spherical harmonic
basis functions) that has been described and evaluated over the years (e.g.,
Kanamitsu, 1989; Yang et al., 2006; Sela, 2009; Yoo et al., 2012, 2013).
Shortwave and longwave radiation are parameterized using the Rapid Radiative
Transfer Models (RRTMG) RRTMG_SW (v3.8) and RRTMG_LW (updated based on
AER's version 4.8), respectively, developed at AER Inc.
(
The datasets used include Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) aerosol optical depth (AOD) data, Climate Prediction Center (CPC) unified gauge-based precipitation data, and the NCEP GFS precipitation forecast data for the year 2015 in three countries: China, the US, and Australia. Other datasets used include long-term NCEP Global Ensemble Forecast System (GEFS) precipitation forecast data, ground-based observations of precipitation and visibility, water vapor and convective available potential energy (CAPE) sounding datasets, and satellite-retrieved aerosol and cloud properties for a small region of Fujian Province in China chosen for more detailed study.
The MERRA-2 aerosol reanalysis (Randles et al., 2016) is an upgrade of the
off-line aerosol reanalysis called MERRAero (da Silva et al., 2011; Rienecker
et al., 2011; Jiang et al., 2016). The aerosol module in MERRAero is based on
the GOCART model (Chin et al., 2002). The AOD observing system sensors extend
from the Moderate Resolution Imaging Spectroradiometer (MODIS) Neural Net
Retrieval (NNR) in MERRAero to a combination of the Advanced Very High
Resolution Radiometer (AVHRR) NNR,
the Aerosol Robotic Network (AERONET), the Multi-angle Imaging SpectroRadiometer (MISR), the
MODIS Terra NNR, and the MODIS Aqua NNR in the MERRA-2 aerosol reanalysis.
More details about the MERRA-2 aerosol reanalysis can be found in Randles et
al. (2016). Three-hourly total aerosol extinction AOD data at 550 nm at a
resolution of 0.625
A unified suite of precipitation analysis products that
include a gauge-based analysis of
global daily precipitation over land was assembled at NOAA's CPC
(
The NWP model forecast data used are 3-hourly rainfall forecasts from
the NOAA NCEP GFS model initialized at 00:00 coordinated universal time (UTC) and
accumulated for 24 h in the three countries chosen for study. The relative
humidity (RH) at 850 hPa and the liquid water path (LWP) calculated
following Yoo et al. (2012) are used, corresponding to the precipitation
record in the three countries at a 0.5
Ground meteorological data acquired in Fujian Province from 1980 to 2009 are
used in this study. Figure 1 shows the locations of the 67 meteorological
stations measuring precipitation. Sixteen of these stations also collect
visibility data four times a day. Daily mean data are used here. Visibility
has been used as proxy for aerosol loading in China in several studies
(Rosenfeld et al., 2007; Yang et al., 2013; Yang and Li, 2014). The main
advantage is the long measurement record under all sky conditions. However,
there are some limitations, e.g., the uncertainty due to humans making the
observations and the influence of aerosol hygroscopic growth. To remove the
humidity influence on visibility, visibility was corrected for RH (Charlson,
1969; Appel et al., 1985) using the formula adopted by Rosenfeld et
al. (2007) when RH falls between 40 and 99 %:
To analyze water vapor and atmospheric stability effects on precipitation,
data collected twice a day (at 00:00 and 12:00 UTC) from three atmospheric
sounding stations (Xiamen, 24.48
Locations of 67 stations measuring precipitation in Fujian Province. Plus symbols show the locations of the 16 stations where visibility measurements are also made. This figure was plotted using the equidistant cylindrical projection.
CloudSat and Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from 2006 to 2010 amassed over Fujian Province
(22.5–28.5
Aqua MODIS retrievals of cloud droplet size and LWP for liquid clouds (clouds
with cloud-top temperatures (CTTs) greater than 273 K) collected over Fujian
Province from 2003 to 2012 are used. Errors in satellite retrievals of AOD
such as cloud contamination (Kaufman et al., 2005; Zhang et al., 2005)
introduce uncertainties in the aerosol–cloud relationship (Gryspeerdt et
al., 2014a, b). We use MODIS Level 3 AOD with AOD
Definitions of warm- and cold-base mixed-phase clouds and liquid clouds.
CPC unified gauge-based daily precipitation data at a
0.5
For the long-term analysis focused on Fujian, China, the NWP model reforecast
precipitation amount accumulated over the period of 12 to 36 h from the
00:00 UTC run at 6-hourly intervals and at a
1
Based on the definitions of the China Meteorological Administration,
precipitation data are classified into four groups according to the daily
rain amount: light rain (0.1–9.9 mm d
Contingency table.
Table 1 summarizes the cloud types considered in the Fujian Province
analysis. Deep mixed-phase clouds are defined as clouds with cloud-base
temperatures (CBTs)
Quantitative precipitation forecast scores developed by NCEP are used in the
evaluation. Table 2 is a contingency table based on documents from the World
Climate Research Programme
(
Under limited ranges of LWP or RH, the top and bottom one-third of AOD
values denote polluted and clean subsets of data. To obtain the forecast
skill under a particular pollution condition, the ETS and the BIAS for clean
and polluted conditions are calculated as
Annual mean precipitation differences (in mm d
The standard deviation of the precipitation bias between the GFS model and
CPC gauge data is calculated as
The relative difference between the forecast precipitation and observations
is calculated as
For the long-term analysis, trends in a particular parameter are defined as the relative change in the parameter (in %) over each successive decade (Lin and Zhao, 2009). The Mann–Kendall method is used to test the significance of the trend.
The CPC gauge-based precipitation analysis from 2015 is used to evaluate the
GFS precipitation forecast. Figure 2 shows the annual mean precipitation
difference between the GFS model and the CPC analysis for three countries,
i.e., China, the US, and Australia, for the year 2015. Values above (below) 0
represent the overestimation (underestimation) of precipitation. In China
(Fig. 2a), the GFS model overestimates the mean daily rainfall mostly in
southwest China, especially in Sichuan, Yunnan, and Guizhou provinces (by
Annual mean relative difference (in mm d
Figure 3 shows the annual mean relative difference between forecast
precipitation and observations for light rain (0.1–10 mm d
Mean relative difference in precipitation between forecast and
observed daily light (
In principle, the underestimation and overestimation at different rainfall
levels (Figs. 3 and 4) may be linked to AOD conditions, as elaborated in the
introduction of previous studies (cf. the review of Tao et al., 2012). The
standard deviation of the forecast bias at each grid point in the three
countries is calculated to further examine the links between the model bias
and AOD. Aerosols tend to polarize precipitation by suppressing light rain
and enhancing heavy rain and thus increase the standard deviation. The
calculation of the standard deviation of the forecast difference is based on
Eq. (7). Figure 5 shows the relationship between the standard deviation and
AOD in the three countries. Each point represents a grid box. The standard
deviation and AOD has a significant positive correlation in the three
countries with correlation coefficients of 0.5602, 0.6522, and 0.5182 for
Australia, the US, and China, respectively. This suggests that the degree of
disparity of the forecast error is larger for grids with high aerosol
loading. The slopes of the best-fit lines are 75.23 for relatively clean
Australia (maximum AOD
Standard deviations of the daily precipitation difference as a
function of aerosol optical depth for
The ETS and BIAS are used to examine the model performance under clean and polluted conditions for different AOD bins with fixed LWP (Fig. 6a and c) or RH (Fig. 6b and d) in the three countries. For a particular LWP or RH condition, the top and bottom one-third of AOD values are defined as polluted and clean subsets of data. In Fig. 6a and b, ETS increases as the LWP or RH increases. This is because large-scale precipitation is diagnosed from cloud mixing ratios. The ETS are smaller for the polluted scenario than for the clean scenario, especially under high-LWP or high-RH conditions. In Fig. 6c and d, the BIAS decreases under polluted conditions compared with the BIAS under clean conditions. The decreases in ETS and BIAS under polluted conditions suggest that AOD influences the model rainfall forecast.
Equitable threat scores
Mean relative precipitation differences between forecast and
observed daily light (
The model performance differs under different conditions, e.g., initial and dynamic settings, and weather regimes. A long-term statistical evaluation of rainfall forecasts for Fujian Province is made to mitigate these fluctuations in the model forecast accuracy. Model data from 1985 to 2010 are used to calculate the relative difference based on Eq. (8). Figure 7 shows the mean relative difference between forecast and observed precipitation for different rain rates from the 67 stations in Fujian Province for all seasons and for summer only. Figure 7a shows that there is 114.36 % more precipitation forecast by the NCEP GEFS model than observed for the light rain cases. For moderate rain, heavy rain, and very heavy rain cases, 29.20, 41.74, and 59.30 % less precipitation than observed, respectively, was forecast. The underestimation of moderate rain (46.88 %), heavy rain (59.58 %), and very heavy rain (70.16 %) is even larger in summer (Fig. 7b).
Seasonally averaged trends (percent change per decade) in daily rain amount
and frequency over Fujian Province from 1980 to 2009 are calculated. Only the
results for rain amount are shown in Fig. 8 because the frequency results
bear a close resemblance. Cross-hatched bars represent data at a confidence
level greater than 95 %. In spring, daily rain amounts decreased over
time, ranging from
Trends (percent change per decade) in mean daily light rain
(
Annual mean visibilities in
Correlation coefficients from linear regressions of visibility and different rain amount types for all seasons.
Correlation coefficients from linear regressions of visibility and different occurrence frequencies of rain amount type for all seasons.
Same as Fig. 9, except for precipitable water vapor.
Same as Fig. 9, except for convective available potential energy (CAPE).
Reasons for the difference between modeled and observed precipitation are examined in terms of aerosol effects, water vapor, and CAPE. Time series of visibility over the period of 1980–2009 are shown in Fig. 9. Visibility has declined steadily in all seasons but summer, during which there was a short-lived increasing trend from 1992 to 1997. The linear declining trends are statistically significant at the 95 % confidence level. The greatest reduction is seen during the summer, especially after 1997. Tables 3 and 4 summarize the correlation between visibility and precipitation amount and frequency, respectively. A positive (negative) correlation between visibility and precipitation means a negative (positive) correlation between aerosol concentration and precipitation. Values with an asterisk represent data at a confidence level greater than 95 %. For light rain, the correlations between daily rain amount and visibility (Table 3) and between rain frequency and visibility (Table 4) are positive for all seasons. For heavy rain to very heavy rain, the correlations between visibility and daily rain amount (Table 3), as well as frequency (Table 4), are negative in summer.
The water vapor amount and atmospheric stability are important factors
related to precipitation. To analyze the potential contributions of these
factors to the forecast bias, their effects on precipitation are examined.
Data from three atmospheric sounding stations (Xiamen, 24.48
Aerosols can influence precipitation through warm- and cold-rain processes
(Tao et al., 2012). Cloud droplet size, LWP for clouds with CTT greater than
273 K, and AOD at 550 nm retrieved from the Aqua MODIS platform over Fujian
Province during the period of 2003–2012 are used to examine the impact of
aerosols on cloud effective radius (CER). Figure 12 shows CER as a function
of AOD for liquid clouds with different LWPs. When the AOD is small
(
Cloud effective radius as a function of aerosol optical depth for
liquid clouds (clouds with top temperatures greater than 273 K) in Fujian
Province, China. Blue triangles represent cases where the liquid water path
(LWP) is less than 50 g m
Cloud-top temperature as a function of aerosol optical depth for
Time series of regionally averaged daily rainfall amount in Fujian
Province, China, in
Several observational and model studies suggest that smaller cloud particles are more likely to ascend to above the freezing level, releasing latent heat and invigorating deep convection (Rosenfeld et al., 2008; Li et al., 2011) while suppressing shallow convection. CTTs and CBTs, converted from CloudSat–CALIPSO measurements of cloud-top and base heights, in Fujian Province from 2006 to 2010 are used to study the impact of aerosols on the cloud development of different clouds. Figure 13 shows CTT as a function of AOD for liquid and warm- and cold-base mixed-phase clouds. Definitions of the different cloud types are summarized in Table 1, which is taken from Li et al. (2011). Left-hand ordinates are for liquid clouds, while right-hand ordinates are for warm-base and cold-base mixed-phase clouds. For all seasons (Fig. 13a), CTTs of warm-base mixed-phase clouds are lower than those of cold-base mixed-phase clouds. Warm-base mixed-phase CTTs decrease with increasing AOD, which indicates that cloud-top heights have increased. For cold-base mixed-phase clouds, variations in CTT with AOD are not obvious. For liquid clouds, CTTs increase slightly with AOD, which means that the development of liquid clouds is suppressed when AOD increases. The negative slope of the linear relationship between CTT and AOD for warm-base mixed-phase clouds and the positive slope of the linear relationship between CTT and AOD for liquid clouds are both stronger in summer (Fig. 13b). This suggests that aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds, with little influence on cold-base mixed-phase clouds. These effects of aerosols on summertime cloud development are more obvious, likely because convective clouds occur more frequently during the summertime in Fujian Province.
These results agree with those from a ground-based study using ARM Southern Great Plains data (Li et al., 2011) and from tropical region studies using CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data (Niu and Li, 2012; Peng et al., 2016). The impact of aerosols on different types of clouds may lead to light-rain suppression and heavier-rain enhancement. If the GFS model neglects aerosol effects, overestimations of light rain and underestimations of heavy to very heavy rain may be forecast, especially in summer. For example, Fig. 14 shows time series of regionally averaged daily modeled and observed precipitation in 2001. Modeled and observed precipitation amounts over the region agree well in spring and winter while modeled precipitation amounts are greater than observations for light rain in autumn. Note that modeled precipitation amounts are significantly less than observed precipitation amounts over the region in summer when deep convective clouds and heavy to very heavy rain tends to occur. Although there are many reasons for the difference between modeled and observed precipitation, these results suggest that to some extent, the neglect of aerosol effects may contribute to the model rainfall forecast bias.
ACIs have been recognized as playing a vital role in precipitation but have not been considered in the NCEP GFS model yet. For more efficient and accurate forecasts, new physical schemes are being incorporated into the NCEP's Next Generation Global Prediction System. As a benchmark evaluation of model results that exclude aerosol effects, the operational precipitation forecast (before any ACIs are included) is evaluated using multiple datasets with the goal of determining if there is any link between the model forecast bias and aerosol loading. Multiple datasets are used, including ground-based precipitation and visibility datasets, Aqua Moderate Resolution Imaging Spectroradiometer products, CloudSat–CALIPSO retrievals of cloud-base and cloud-top heights, Modern-Era Retrospective analysis for Research and Applications Version 2 model simulations of AOD, and GFS forecast datasets.
Operational daily precipitation forecasts for the year 2015 in three countries, i.e., Australia, the US, and China, were evaluated. The model overestimates light rain and underestimates moderate rain, heavy rain, and very heavy rain. The underestimation of precipitation in summer is even larger. This is consistent qualitatively with the expected results because the model does not account for aerosol effects on precipitation, i.e., the inhibition of light rain and the enhancement of heavy rain by aerosols. The standard deviations of forecast differences are generally positively correlated with increasing aerosol loadings in the three countries. Equitable threat scores and BIAS scores decrease for the polluted scenario.
An analysis of long-term measurements from Fujian Province, China, was done. Light-rain overestimation and moderate-, heavy-, and very heavy-rain underestimations from the Global Ensemble Forecast System were also seen. The underestimation for stronger rainfall was larger in the summertime. Increasing trends for heavy and very heavy rain in summer and decreasing trends for light rainfall in other seasons were significant from 1980 to 2009. Long-term analyses show that neither water vapor nor convective available potential energy can explain these trends. Satellite datasets amassed in Fujian Province from 2006 to 2010 were used to shed more light on the impact of aerosols on cloud and precipitation. As implied by the Twomey effect, cloud effective radii decrease with increasing AOD, which likely suppresses light rain and enhances heavy rain. This may contribute to the model forecast bias to some extent. The underestimation of heavy rain in summer most likely occurs because deep convective clouds occur more frequently during the summertime in Fujian Province.
It remains an open question how neglecting ACI in the operational forecast model impacts model biases. This study is arguably the first attempt at evaluating numerical weather prediction forecast errors in terms of the potential effects of aerosols. A more rigorous and systematic evaluation to gain insights into the model is needed. Toward this goal, case-based investigations using rich instantaneous measurements are currently underway.
Forecast data are from the NOAA NOMADS
(
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
This study was supported by the Ministry of Science and Technology of China
(2013CB955804), the National Science Foundations of China (91544217) and of the
US (AGS1534670), the Fundamental Research Funds for the Central Universities
of China (312231103), the State Key Laboratory of Earth Surface Processes and
Resource Ecology (2015-TDZD-090), and NOAA (NA15NWS4680011). We would like to
thank the NASA Global Modeling and Assimilation Office
(
We would also like to thank Shrinivas Moorthi and Jun Wang from NOAA, Sarah Lu from State University of New York, Albany, Seoung-Soo Lee from the University of Maryland, and Duoying Ji and Lanning Wang from Beijing Normal University for their discussions regarding this study. We especially appreciate the help given by Jongil Han in understanding the GFS/GEFS models and data products and the guidance provided by Hye-Lim Yoo. We also greatly appreciate the valuable comments from the anonymous reviewers. Edited by: Jianping Huang Reviewed by: two anonymous referees