This manuscript couples the dynamic TenStream 3D radiative transfer solver to the PALM large-eddy simulation model and investigates how 3D versus 1D radiative transfer treatments affect shallow cumulus cloud field evolution over a full diurnal cycle. It also serves as an online validation of the dynamic TenStream solver against the original, more expensive TenStream implementation. The manuscript provides a number of novel and significant insights including:

• Demonstrating for the first time that dynamic surface heterogeneities from 3D radiative transfer (RT) with a realistic diurnal cycle can produce cloud streets
• Cloud cover with a 1D treatment of RT produces higher cloud coverage than simulations with a 3D RT treatment for high coverage (40-50%) shallow cumulus days
• The importance of 3D RT in the longwave for nighttime cloud field properties, with the clouds with 1D RT developing significantly higher average LWP and deeper fields, and converging toward full coverage sooner than clouds with 3D RT. 
• Longwave 3D RT effects impact mean surface latent heat flux; reduced thermal emission from the surface in the 3D RT simulations (due to thermal emission from clear and not only cloudy regions in the 3D RT simulations) is offset mainly by increased latent heat flux, mediated by increases in surface skin temperature.

Separately from the physical insights provided by the manuscript, this work also includes validation of the dynamic TenStream solver against the original version:

• For a high cloud coverage shallow cumulus case in which the dynamic TenStream solver is expected to perform well, the dynamic TenStream shows similar daytime cloud cover, average LWP, and cloud depth as the original TenStream solver and modestly diverging cloud cover and LWP at nighttime.

Below are some suggestions to improve this already strong manuscript.

Manuscript organization

The manuscript tackles two objectives: (1) to validate the performance of the dynamic TenStream solver against the original, higher-fidelity TenStream solver in an online setting and (2) to discuss how 3D versus 1D treatments of radiation impact cloud field statistics of shallow cumulus. While there are some subtle lessons about the cloud field to be learned from the differences between the dynamic and original TenStream solvers (i.e. lingering surface enhancements in the dynamic version), for the most part these two objectives appear separate. It would benefit the reader for the authors to treat them as such, particularly within sections 3.2 and 3.3. Subsections might help here.

Emphasis of novel results

Several of the manuscript's most interesting results could be given greater prominence.

This work demonstrates for the first time that radiatively driven cloud streets can develop with 3D RT under realistic diurnal conditions, clearly distinguishing it from prior work by Jakub and Mayer (2017) in which solar zenith angle was kept fixed. The manuscript would benefit from better highlighting this result.

This reader is fascinated by the result that for higher cloud cover days, cloud coverage for simulations with a 1D treatment of radiation is higher than in simulations with a 3D treatment of radiation (supported by the results of Tijhuis et al. 2024). Why might this be? Further discussion would be greatly appreciated. 

The manuscript shows that longwave 3D RT impacts the surface energy budget via reduced thermal emission, propagating into increased surface latent heat fluxes. This higher moisture flux into the atmosphere from the surface is hypothesized by the authors to partially explain the higher LWP in the 3D RT simulations. To this reviewer’s knowledge, this mechanism is novel in the literature and deserves greater focus. Do the authors believe it is equally significant to the mechanism whereby clouds in the 3D simulations are positioned over areas of enhanced net surface irradiance, strengthening rather than suppressing the associated updrafts? Can the two mechanisms be isolated to assess their relative importance? If computational resources allow, simulations with 3D RT for shortwave only and with 3D RT for longwave only would be of interest. Work from Veerman et al. 2022 and Tijhuis et al. 2024 suggests that the shortwave mechanism may be dominant as they see substantial LWP increases with 3D RT without longwave effects.

The differences in 3D versus 1D shortwave surface flux fields, as described at the start of section 3.3, are well documented in the literature. A briefer introduction of these concepts might allow the authors to place stronger emphasis on (a) the connection between surface flux heterogeneities and cloud field evolution (b) what the model artifacts of the dynamic TenStream solver and resulting cloud field differences can teach us about this mechanism.

Statistical rigor of dynamic TenStream validation

Dynamic TenStream and original TenStream solver differences should not be evaluated solely by comparing their differences with those between 1D and original TenStream; instead the authors should leverage their (modest) ensemble, asking if the observed differences are greater or less than the variability observed for original TenStream. Using additional ensemble members, if computationally feasible, would allow for a more statistically rigorous analysis of the signal to noise ratio of these differences. Is the top row of Figure 6 a useful and fair comparison for the performance of the dynamic TenStream model? Does it provide additional information beyond Figure 4?

General suggestions and questions

The authors should take care to distinguish between surface field lag artifacts of the dynamic TenStream solver and the physical displacement of cloud shadow from cloud root that occurs at moderate solar zenith angles in the original TenStream results (lines 415–420). The former is a solver artifact arising from incomplete solves; the latter is a real 3D radiative effect. The current text could more clearly distinguish between the two.

What is the surface heat capacity C₀ used in the PALM land surface model for this setup? If it is small, would the authors not expect a near-instantaneous response of the surface energy budget to changes in the radiative field (lines 425–435)?

The authors coupled the dynamic TenStream solver, a recently developed and computationally efficient 3D radiative transfer approximation, to the PALM large-eddy simulation model. The authors then study how 3D radiative transfers affects the development of cloud field properties and organization, and evaluate how well the dynamic TenStream solver perform compared to the original TenStream solver. The paper is interesting and generally well-written. Subject to the following comments, I consider it suitable for publication.

Main comments:

1. What are the initial profiles, surface values and other parameters based on? To me, the start of section 2.1.2 “simulations were set-up for the geographic location of Munich … started on 14 June 2023” suggests you are reproducing a realistic case study here, but it is not clear too what extent the settings and initial profiles are based on observations or whether it is somewhat idealized case study. If it is largely based on observations, are there any cloud (lidar, satellite) or surface irradiance observations available to compare against simulation results?
2. The results section alternates between discussing 1D vs 3D radiative effects and discussing the original vs dynamic Tenstream. In find that this alternation breaks the logical flow of the text and makes the discussion of the results sometimes a bit confusing, as it is not always clear whether “3D” refers to the dynamic or original TenStream solver. Since the 3D effects (compared to 1D) are sufficiently large compared to the differences between the two Tenstream solvers, I would separate the two parts more clearly: first discuss how and why switching from 1D to 3D (dynamic Tenstream) changes your cloud field, secondly discuss how any errors between the dynamic and original Tenstream solver may affect your results.
3. Throughout the manuscript, I would stress more explicitly how the understanding of 3D radiative effects from this work extend beyond the work of e.g. Jakub & Mayer (2017), Veerman et al. (2020,2022), Tijhuis et al. (2024). That is, better highlight the novel aspects regarding 3D effects. For example, the inclusion of 3D longwave radiative effects, cloud streets that change orientation following the sun, and, if considered realistic (see later comment), 3D effects that continue into the night.

additional comments

1. 325 It may be worth further looking into this nighttime cloud layer, whether we can consider it realistic and why the cloud do not disappear. Besides looking at soil moisture, did you consider the initial profiles. Could you maybe quantify the convection in the sub-cloud layer, perhaps time series of the vertical velocity variance may indicate when the convections dies out.
2. Consider omitting section 2.2.1, to me a separate section detailing the MBE is not necessary.
3. Runtime is briefly discussed in the summary, referring to Maier et al. 2024. Please elaborate on computational performance and limitations (run time, memory) of the simulations with 1D and with both 3D solvers earlier on in the manuscript (methods or results section).
4. Figure 6: it is not clear to me what the added value is of this figure over figure 4
5. Since you have 3D longwave and that turns out to be important, could you add spatial plots of net longwave irradiance (similar to fig 7 or 9) to aid the reader.