Noah Land Surface Model Research Articles

Use of LCZs with urban canopy modeling to evaluate urban growth effects on meteorological conditions in the Salt Lake Valley

Urban Canopy Models (UCMs) can be used to represent urban characteristics on a mesoscale without accounting for effects of individual buildings. A WRF-based single-layer urban canopy model (SLUCM) was validated for the greater Salt Lake City area (GSLA) using measurements from two weather stations for a one-week period during summer 2019. A baseline model based on current GSLA properties developed using the Noah land surface model, SLUCM, and refined urban classifications based on WUDAPT Local Climate Zones (LCZs) produced acceptable predictions and computational times. The baseline model was validated using measured meteorological data and clearly showed diurnal heating patterns. Three scenarios representing multi-decade growth were defined by modifying LCZs in different parts of the GSLA. Compared to baseline values, results showed increased urban density increased local nighttime temperatures 5–10 °C depending on the extent of growth, but did not affect daytime temperatures. This was due to changes in LCZ anthropogenic heating rates and sky view factors which were most noticeable during early nighttime hours, consistent with atmospheric heat island behavior. Predicted 2-m relative humidity and 10-m wind speeds changed minimally.

Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity

Abstract. Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts.

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

AbstractSoil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions

Abstract. In this study, the Noah land surface model used in conjunction with the Mellor–Yamada–Janjić surface layer scheme (hereafter, Noah-MYJ) and the Noah multiphysics scheme (Noah-MP) from the Weather Research and Forecasting (WRF) 4.5.1 mesoscale model are evaluated with regard to their performance in reproducing positive temperature gradients over forested areas in the Arctic winter. First, simplified versions of the WRF schemes, recoded in Python, are compared with conceptual models of the surface layer in order to gain insight into the dependence of the temperature gradient on the wind speed at the top of the surface layer. It is shown that the WRF schemes place strong limits on the turbulent collapse, leading to lower surface temperature gradient at low wind speeds than in the conceptual models. We implemented modifications to the WRF schemes to correct this effect. The original and modified versions of Noah-MYJ and Noah-MP are then evaluated compared to long-term measurements at the Ameriflux Poker Flat Research Range, a forest site in interior Alaska. Noah-MP is found to perform better than Noah-MYJ because the former is a two-layer model which explicitly takes into account the effect of the forest canopy. Indeed, a non-negligible temperature gradient is maintained below the canopy at high wind speeds, leading to overall larger gradients than in the absence of vegetation. Furthermore, the modified versions are found to perform better than the original versions of each scheme because they better reproduce strong temperature gradients at low wind speeds.

Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model‐Physics Coupling Framework

AbstractAccurate estimation of snow accumulation and melt is a critical part of decision‐making in snow‐dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah‐MP and Noah. The original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah‐Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah‐MP. The Coupled Noah‐MP‐Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Impact of Land Surface Snow Processes on the Arctic Stable Boundary Layer

Abstract The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Analysis of Effects of Spatial Distributed Soil Properties and Soil Moisture Behavior on Hourly Streamflow Estimate through the Integration of SWAT and LSM

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.

Are the Noah and Noah-MP land surface models accurate for frozen soil conditions?

The accurate prediction of frozen soil conditions is a critical component of dynamic terrain characterization for engineering operations in cold regions. Currently the trusted resource for dynamic terrain within the US military uses gridded global soil conditions (i.e., temperature and moisture) generated by the Land Information System (LIS) architecture. LIS combines static terrain data with trusted surface weather information into the Noah land surface model to generate a continuous representation of the global surface conditions. Both the input weather data and the model output land surface state are adjusted to match the observed state using an advanced ensemble Kalman filter data assimilation technique to incorporate in situ and remote sensing observations. While this product has been robustly evaluated under unfrozen conditions only sporadic studies have investigated its ability to accurately simulate freeze/thaw cycles, and soil temperature and moisture under frozen conditions. Here, we present a broad comparison and evaluation of the simulated soil state against an in situ network of soil temperature and moisture distributed across the Continental United States that focuses on the winter season. This comparison includes both the Noah and the newer Noah with multi-parameterization (Noah-MP) land surface models. A comparison of the two models reveals substantial differences in simulated soil moisture, frozen content, and frozen soil strength. In general, Noah predicts colder soils with deeper frost depths, and frozen soils that are slightly stronger than Noah-MP. Initial results evaluating Noah and Noah-MP against in situ observations show that both models have significantly degraded model performance for frozen soil conditions compared to their performance for unfrozen soil. In particular, Noah has a significant and persistent cold bias of approximately −3 K when the soil temperature is below freezing. This cold bias is not present in Noah-MP and further analysis shows that this improvement was almost entirely caused by a better representation of thermal conduction through snow in Noah-MP. Specifically, we find that the bulk effective thermal conductivity of a snowpack is 2× greater in Noah, which allows for greater surface heat loss during the winter and, consequently, lower soil temperatures. This analysis reveals that the more advanced multi-layer snow model in Noah-MP substantially improves the representation of soil temperature during the cold season.

Evaluation of Land–Atmosphere Coupling Processes and Climatological Bias in the UFS Global Coupled Model

Abstract 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.

A spatially explicit interpretable machine-learning method to track dissolved inorganic nitrogen pollution in a coastal watershed

Sustainable water management requires an in-depth understanding of dissolved inorganic nitrogen (DIN) in watersheds. However, it remains challenging in the context of intensifying human-induced and naturally impacted stresses in a changing environment. A spatially explicit interpretable machine-learning method was proposed based on the annual variation in land use/cover, Catchment Land Surface Model, Noah land surface model, population dynamics, and nightlight in a coastal Chinese watershed. The proposed model was effective for delineating DIN processes and sources in watersheds. The d (index of agreement), R2 (coefficient of determination), |PBIAS| (percent bias), and KGE (Kling-Gupta efficiency)during the training period were 0.91–0.98, 0.76–0.96, 0.38–3.12, and 0.73–0.87, respectively. The d, R2, |PBIAS|, and KGE during the testing period were 0.85–0.94, 0.56–0.79, 3.59–6.18, and 0.65–0.86, respectively. NH4+-N in the watersheds may be strongly related to the effects of urbanization within the watersheds while the agricultural activities may modify the patterns of NO3–-N in the watersheds. The NH4+-N was highly related to the urbanization effects, which contributed 20 %–30 % of the riverine NH4+-N in the North River. In contrast, agricultural activities may modify patterns of NO3–-N in watersheds, and agricultural activities in the West River contributed 50 %–70 % riverine NO3–-N in the watershed. Moreover, urbanization may alter the water content, soil properties, and regional climate patterns within the watersheds, while repeated DIN input through agricultural activities in the agricultural watersheds may change the nitrogen processes in the soil and groundwater. Paired with agricultural activities, dams or reservoirs may amplify NH4+-N to watersheds with modifying the advection and diffusion of DIN from agricultural activities, and increasing sediments in the watersheds. This study demonstrated the potentials of the proposed method in tracking DIN pollution and provided new insights into nutrient management in the watersheds under intensifying human-nature interactions.

Оценка точности воспроизведения метеорологических характеристик по модели WRF для акватории Каспийского моря

The paper presents the accuracy estimates for the simulation of meteorological characteristics obtained from the WRF model with a spatial resolution of 6 km adapted for the Caspian Sea. It was used to simulate meteorological characteristics using various parameterizations of physical processes available in the WRF model. The model was verified using data from coastal weather stations for 2021. It was found that the best results in reproducing meteorological characteristics are achieved using the Thompson graupel scheme as a parameterization of physical processes, the Tiedtke scheme as a parameterization of convection, the RRTMG scheme as a parameterization of long-wave and short-wave radiation, the MYJ scheme as a boundary layer parameterization, and the Noah Land-Surface Model as a surface layer parameterization. An analysis of the simulation results versus observational data from weather stations showed that the model correctly reproduces the modulus and direction of wind speed, air temperature, and sea-level pressure. It was found that the model correctly reproduces not only the average characteristics of wind speed, but also its extreme values, which is especially important when modeling storm surges in the Caspian Sea. Keywords: Caspian Sea, regional nonhydrostatic atmosphere circulation model, numerical methods

Assimilation of airborne gamma observations provides utility for snow estimation in forested environments

Abstract. An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.

Urban WRF-Chem evaluation over a high-altitude tropical city

Morphology and grid resolution are important aspects that need to be considered in urban modeling applications, since together with buildings they induce a direct effect on wind and dispersion of pollutants over urban areas. In this study, we evaluate high-resolution simulations of a multi-layer urban canopy model (UCM) based on a local climate zone (LCZ) classification coupled to the Weather Research and Forecasting model with Chemistry (WRF-Chem), in the local meteorological conditions and air quality pollutants of a highly urbanized megacity. This modeling system, known as Building Effect Parameterization (BEP) considers the effects of buildings’ vertical and horizontal surfaces on the momentum that considerably impacts the lower part of the urban boundary layer (UBL). Simulations of the urbanized model (WRFu) were compared against a Noah land surface model (Noah LSM) with no urban physics (WRF) for the same period. It was observed that the LCZ classification and urban parameterization coupled to the model have a direct influence in meteorological parameters and pollutant concentrations. Urban simulations of temperature and wind speed showed higher sensitivity to initial and boundary conditions, increasing the correlation with observations and reducing the bias error. An important observation is that emissions drive air quality concentrations despite the improvements in local meteorology.

Convection-Permitting Simulations of Precipitation over the Peruvian Central Andes: Strong Sensitivity to Planetary Boundary Layer Parameterization

Abstract Regional climate dynamical downscaling at convection-permitting resolutions is now practical and has the potential to significantly improve over coarser-resolution simulations, but the former is not necessarily free of systematic biases. The evaluation and optimization of model configurations are therefore important. Twelve simulations at a grid spacing of 3 km using the WRF Model with different microphysics, planetary boundary layer (PBL), and land surface model (LSM) schemes are performed over the Peruvian central Andes during the austral summer, a region with particularly complex terrain. The simulated precipitation is evaluated using rain gauge data and three gridded precipitation datasets. All simulations correctly capture four precipitation hotspots associated with prevailing winds and terrain features along the east slope of the Andes, though they generally overestimate the precipitation intensity. The simulation using Thompson microphysics, Asymmetric Convection Model version 2 (ACM2) PBL, and Noah LSM schemes has the smallest bias. The simulated precipitation is most sensitive to PBL, followed by microphysics, and least sensitive to LSM schemes. The simulated precipitation is generally stronger in simulations using the YSU rather than the MYNN and ACM2 schemes. All simulations successfully capture the diurnal precipitation peak time mainly in the afternoon over the Peruvian central Andes and in the early morning along the east slope. However, there are significant differences over the western Amazon basin, where the precipitation peak occurs primarily in the late afternoon. Simulations using YSU exhibit a 4–8-h delay in the precipitation peak over the western Amazon basin, consistent with their stronger and more persistent low-level jets. These results provide guidance on the optimal configuration of a dynamical downscaling of global climate projections for the Peruvian central Andes.

Using satellite data to estimate the evapotranspiration in Brazilian basins: From 2003 to 2016

Satellite data is essential to environmental investigations, especially related to hydroclimatic issues. In the last years, many missions have been cooperating with these studies. This research aimed to estimate evapotranspiration in five major Brazilian basins (Amazon, Paraguay, Paraná, São Francisco and Tocantins). The methodology was based on data from the GRACE mission (2003–2016) and hydroclimatic variables such as the Noah Land Surface Model L4 model runoff and precipitation from the Tropical Rainfall Measuring Mission. In addition, the results were compared to the Global Land Data Assimilation System (GLDAS) model. In the Amazon, Tocantins, and Paraguay basins, the Pearson correlation was less than 0.3, while in the Paraná and São Francisco basins, the correlation was more than 0.5.

Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model

The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.

Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China

In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations were collected to investigate the typical land–atmosphere coupling chain during this modeled rainstorm by using various local coupling metrics and introduced ensemble statistical metrics. The results show that (1) except for the stratospheric thermodynamics and the surface temperature over mountain areas, the main characteristics of the mid-low atmospheric layers and the surface have been well captured in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, an early afternoon weak PBL warming factor of around 2, a noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contributions of around 100 and 280 W/m2, respectively, and significant afternoon soil-surface latent flux coupling; and (3) an overall negative soil–rainfall relationship can be identified from the ensemble metrics in which the moist static energy is more significant than PBL height, and this is consistent with the significance of daytime surface moistening indicated by local coupling metrics. Taking the multi-process chain in chronological order, the wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noon period buoyant mixing within weak moist heating; however, this is suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which further significantly shapes the spatial distribution of the statistical metrics. These quantitatively described local daytime couplings highlight the potential local application of promoting public weather forecasting efforts, while the high spatial differences in the coupling indicate the more applicable threshold diagnoses within finer-scale spatial investigations.

Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.