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Persistent strong winds are a common feature within the near‐surface layer of tropical cyclones, which can induce pronounced horizontal motion as raindrops descend. However, current state‐of‐art microphysics schemes typically only consider the vertical motion of raindrops, ultimately failing to accurately simulate the collisional outcomes of raindrops and the associated raindrop size distributions (RSDs) under strong winds. For instance, the original bin microphysics scheme was unable to successfully reproduced the evolution of RSDs with decreasing height, as measured from the multi‐layer observations during the landfall of Typhoon Pakhar (2017). Thus, this study introduces a modified bin microphysics scheme that incorporates the influence of horizontal wind speeds, vertical wind shear and smaller‐scale turbulence on the total movement velocity (including horizontal and vertical components) of raindrops, and consequently on their collisional outcomes. This modification demonstrates a remarkable improvement in the representation of the intrinsic variation in RSDs with decreasing height under strong‐wind conditions. Plain Language Summary Microphysical processes play a critical role in determining rainfall intensity, so accurately simulating these processes is essential for improving the accuracy of rainfall forecasts. However, current state‐of‐art microphysics schemes primarily consider only the vertical motion of raindrops and the outcomes of gravitational collision, which can lead to forecast inaccuracies. Based on multi‐layer observations, this study found that raindrops exhibit sub‐terminal velocity (smaller than their terminal velocity) and pronounced horizontal motion, influenced by strong horizontal wind speeds, vertical wind shear and smaller‐scale turbulence within the near‐surface layer of tropical cyclones. These factors can significantly change the sedimentation and collisional coalescence/breakup processes of raindrops. Incorporating the effects of strong winds into the parameterization of the coalescence/breakup processes within a bin microphysics scheme has substantially improved the simulation of the observed vertical evolution of RSDs with decreasing height. These findings will enhance the understandings and capacity to reproduce microphysical processes under strong‐wind conditions. Key Points The movement of raindrops is significantly influenced by the winds, leading to the changes in raindrop sedimentation and coalescence/breakup Current microphysics schemes only consider the vertical motion of raindrops, failing to simulate the microphysics under strong winds A bin microphysics scheme was updated to include the wind effects on raindrop total motion and collisions, improving raindrop size distribution representation

The meso‐gamma scale variability of air‐sea sensible heat flux HS$\\text{HS}$was investigated using synchronized flux observations from two tower‐based platforms within 10 km over coastal regions. Observations revealed statistically weak correlations between spatial HS$\\text{HS}$variability and both 10 m wind speed U10${U}_{10}$and air‐sea temperature difference ∆T$\\mathit{{\\increment}}T$ . While U10${U}_{10}$roughly consistent between the two stations, significant ∆T$\\mathit{{\\increment}}T$discrepancies emerged, particularly along the parallel line connecting the two sites. Despite occasional variations at specific measurement points or times, the HS$\\text{HS}$characteristics demonstrated statistical consistency between the two stations. Comparative analysis with the bulk algorithm indicated that bulk HS$\\text{HS}$differences could partially explain observed ∆T$\\mathit{{\\increment}}T$variations. However, observed HS$\\text{HS}$magnitudes substantially exceeded bulk estimates, and the heat flux exchange coefficients derived from observations differ by 1.6 times between the two stations. These findings highlight the necessity for enhanced high‐resolution direct flux measurements to validate parameterization schemes.

The dynamics of atmospheric CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from the terrestrial flux and the influence of atmospheric mixing. In this study we apply the WRF-Chem model configured with the Vegetation Photosynthesis and Respiration Model (VPRM) option for biomass fluxes in China to characterize the dynamics of CO2 in the atmosphere. The online coupled WRF-Chem model is able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. We apply WRF-Chem for a multi-year simulation (2016–2018) with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the spatio-temporal variations of CO2 fluxes and concentrations in China. We find that the spatial distribution of CO2 was dominated by anthropogenic emissions, while its seasonality (with maxima in April 15 ppmv higher than minima in August) was dominated by the terrestrial flux and background CO2. Observations and simulations revealed a consistent increasing trend in column-averaged CO2 (XCO2) of 2.46 ppmv (0.6 %yr-1) resulting from anthropogenic emission growth and biosphere uptake. WRF-Chem successfully reproduced ground-based measurements of surface CO2 concentration with a mean bias of-0.79 ppmv and satellite-derived XCO2 with a mean bias of 0.76 ppmv. The model-simulated seasonality was also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and satellite data, respectively. Tower observations from a background site at Lin'an (30.30∘ N, 119.75∘ E) revealed a strong correlation (-0.98) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmosphericCO2.

Evapotranspiration (ET) plays a key role in agricultural water resources management. However, it is challenging to predict as it is driven by water and energy availability as well as soil, vegetation, and meteorological factors, and models vary widely in complexity and assumptions. Causal discovery methods can identify drivers and interactions based on time‐series data from both observations and models, and can be used as metrics of model “functional performance” that evaluate how models capture source‐target relationships. With many approaches to causal discovery, it is important to compare how functional performance metrics align with predictive accuracy and behave across temporal scales. We compare four methods (Granger causality, Transfer Entropy, PCMCI, and Convergent Cross Mapping) to analyze the functional performance of ET models in a corn‐soybean agricultural landscape based on 7 years of eddy covariance measurements, which we use as an empirical reference benchmark. We identify causal sources, among observed weather and soil variables, for Priestly‐Taylor (PT), Surface Flux Equilibrium (SFE), Soil Water Balance (SWB), and satellite‐based ET products from OpenET, and evaluate how closely model‐derived and observation‐based causal structures align. Methods consistently identify model forcings as sources, but otherwise vary widely in terms of sources and strengths across sub‐hourly to weekly timescales. OpenET products have high functional performance, indicating that they capture key processes although they are not forced by tower observations. Finally, some functional metrics align better with predictive performance than others, which highlights the importance of selecting robust metrics that both capture interactions and align with predictive accuracy.

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Reanalysis products produced at the various centers around the globe are utilized formany different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.

Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg-1 dry biomass burned. The CO / CO2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH4 / CO2 emission ratio was 0.010 ± 0.004, and the CH4 emission factor was 5.3 ± 1.8 g kg-1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH4 emission ratios varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.

In the past decade, the scientific community has seen an increase in the number of global hydrometeorological products. This has been possible with efforts to push continental and global land surface modelling to hyper-resolution applications. As the resolution of these datasets increases, so does the need to compare their estimates against local in-situ measurements. This is particularly important for Brazil, whose large continental-scale domain results in a wide range of climates and biomes. In this study, high-resolution (0.1 to 0.25°) global and regional meteorological datasets are compared against flux tower observations at 11 sites across Brazil (for periods between 1999–2010), covering Brazil's main land cover types (tropical rainforest, woodland savanna, various croplands, and tropical dry forests). The purpose of the study is to assess the quality of four global reanalysis products [ERA5-Land, GLDAS2.0, GLDAS2.1, and MSWEPv2.2] and one regional gridded dataset developed from local interpolation of meteorological variables across the country [Brazilian National Meteorological Database (referred here as BNMD)]. The surface meteorological variables considered were precipitation, air temperature, wind speed, atmospheric pressure, downward shortwave and longwave radiation, and specific humidity. Data products were evaluated for their ability to reproduce the daily and monthly meteorological observations at flux towers. A ranking system for data products was developed based on the Mean Squared Error (MSE). To identify the possible causes for these errors, further analysis was undertaken to determine the contributions of correlation, bias, and variation to the MSE. Results show that, for precipitation, MSWEP outperforms the other datasets at daily scales but at a monthly scale BNMD performs best. For all other variables, ERA5-Land achieved the best ranking (smallest) errors at the daily scale and averaged the best rank for all variables at the monthly scale. GLDAS2.0 performed least well at both temporal scales, however the newer version (GLDAS2.1) was an improvement of its older version for almost every variable assessed. BNMD wind speed and GLDAS2.0 shortwave radiation outperformed the other datasets at a monthly scale. The largest contribution to the MSE at the daily scale for all datasets and variables was the correlation contribution whilst at the monthly scale it was the bias contribution. ERA5-Land is recommended when using multiple hydrometeorological variables to force land-surface models within Brazil.

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS’s ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.