Global Numerical Weather Prediction Research Articles

Examination of Computational Performance and Potential Applications of a Global Numerical Weather Prediction Model MPAS Using KISTI Supercomputer NURION

To predict extreme weather events, we conducted high-resolution global atmosphere modeling and simulation using high-performance computing. Using a new-generation global weather/climate prediction model called MPAS (Model for Prediction Across Scales) with variable resolution, we tested strong scalability on the KISTI (Korea Institute of Science and Technology Information) supercomputer NURION. In addition to assessing computational performance, we simulated three typhoons that occurred in 2019 to analyze the forecast accuracy of MPAS. MPAS results were also applied to force an ADCIRC (The Advanced CIRCulation) + SWAN (Simulating Waves Nearshore) model to predict coastal flooding over southern Korea. The time-integration of MPAS showed excellent scalability up to 4096 cores of NURION KNL (KNight Landing) nodes, but a serious I/O bottleneck issue was still found after trying two additional I/O strategies (i.e., adjusting the stripe count and using a burst buffer). On the other hand, the forecast accuracy of MPAS showed very encouraging results for wind and pressure during typhoons. ADCIRC+SWAN also generated a good estimate of significant wave height for typhoon Mitag. The proposed variable-resolution MPAS model, under an efficient computational environment, could be utilized to predict and understand the highly nonlinear chaotic atmosphere and coastal flooding in typhoons.

Characteristics of convective precipitation over tropical Africa in storm‐resolving global simulations

AbstractWe analyse how the representation of deep convection affects the characteristics of convective precipitation over tropical Africa in global storm‐resolving simulations with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The simulations consist in 48 hr forecasts initiated daily at 0000 UTC for a 40‐day period in August and September 2016, at 9 and 4 km resolution. We find that results agree best with the satellite retrieval Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM (IMERG) when a term for the integrated and scaled total advective moisture tendency is added to the convective instability closure. This allows for a better coupling of the convective parametrization to the mesoscale dynamics and improves precipitation intensity (increased), precipitating area fraction (decreased), the size of individual deep convective systems (decreased) and their propagation (westward), even though the duration of rain events remains underestimated. With explicit deep convection, the duration of rain events is more realistic, but precipitation intensity is significantly overestimated, and extreme rain events (>400 mmday) are three times more likely than in GPM IMERG, causing biases in the organization of deep convection. In particular, individual convective systems are too small, hindering the formation of mesoscale convective systems. These findings are also valid for two other regions: the Maritime Continent and the contiguous United States. Our results suggest that deep convection is not completely resolved at a resolution of 9 or even 4 km, and that some scale‐aware parametrization is still needed for global numerical weather prediction.

Improving Numerical Weather Prediction–Based Near-Cloud Aviation Turbulence Forecasts by Diagnosing Convective Gravity Wave Breaking

AbstractBased on a convective gravity wave drag parameterization scheme in a numerical weather prediction (NWP) model, previously proposed near-cloud turbulence (NCT) diagnostics for better detecting turbulence near convection are tested and evaluated by using global in situ flight data and outputs from the operational global NWP model of the Korea Meteorological Administration for one year (from December 2016 to November 2017). For comparison, 11 widely used clear air turbulence (CAT) diagnostics currently used in operational NWP-based aviation turbulence forecasting systems are separately computed. For selected cases, NCT diagnostics predict more accurately localized turbulence events over convective regions with better intensity, which is clearly distinguished from the turbulence areas diagnosed by conventional CAT diagnostics that they mostly failed to forecast with broad areas and low magnitudes. Although overall performance of NCT diagnostics for one whole year is lower than conventional CAT diagnostics due to the fact that NCT diagnostics exclusively focus on the isolated NCT events, adding the NCT diagnostics to CAT diagnostics improves the performance of aviation turbulence forecasting. Especially in the summertime, performance in terms of an area under the curve (AUC) based on probability of detection statistics is the best (AUC = 0.837 with a 4% increase, compared to conventional CAT forecasts) when the mean of all CAT and NCT diagnostics is used, while performance in terms of root-mean-square error is the best when the maximum among combined CAT and single NCT diagnostic is used. This implies that including NCT diagnostics to currently used NWP-based aviation turbulence forecasting systems should be beneficial for safety of air travel.

FY-3E: The First Operational Meteorological Satellite Mission in an Early Morning Orbit

Fengyun-3E (FY-3E), the world’s first early-morning-orbit meteorological satellite for civil use, was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021. The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation, working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning, morning, and afternoon orbits. The combination of these three satellites will provide global data coverage for numerical weather prediction (NWP) at 6-hour intervals, effectively improving the accuracy and time efficiency of global NWP, which is of great significance to perfect the global earth observing system. In this article, the background and meteorological requirements for the early-morning-orbit satellite are reviewed, and the specifications of the FY-3E satellite, as well as the characteristics of the onboard instrumentation for earth observations, are also introduced. In addition, the ground segment and the retrieved geophysical products are also presented. It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning, mid-morning, and afternoon satellite missions. Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events, the development of improved sampling designs of the diurnal cycle for accurate climate data records, more efficient monitoring of air quality by thermal infrared remote sensing, and the quasi-continuous monitoring of the sun for space weather and climate.

All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System

Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%.

Large tropical cyclone track forecast errors of global numerical weather prediction models in western North Pacific basin

Although tropical cyclone (TC) track forecast errors (TFEs) of operational warning centres have substantially decreased in recent decades, there are still many cases with large TFEs. The International Grand Global Ensemble (TIGGE) data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction (NWP) models. Forty-four TCs in the western North Pacific during the period 2007–2014 with TFEs (+24 to +120 h) larger than the 75th percentile of the annual error distribution (with a total of 93 cases) are identified.Four categories of situations are found to be associated with large TFEs. These include the interaction of the outer structure of the TC with tropical weather systems, the intensity of the TC, the extension of the subtropical high (SH) and the interaction with the westerly trough. The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions, the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE. The performance of different models in different situations is discussed.

Evaluation of Global Wave Probabilities Consistent with Official Forecasts

AbstractThe U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

Invited perspectives: The ECMWF strategy 2021–2030 challenges in the area of natural hazards

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) mission is to deliver high-quality global medium-range numerical weather predictions and monitoring of the Earth system to its member states. The modelling and forecasting of natural hazards are an important part of this mission. Challenges in this area include the integration of innovative observations into the Earth system; realistic representations of water, energy and carbon cycles; coupling and initialisation of all Earth system components; adequate representation of uncertainties; supporting the development of user-specific products to enable optimal decision-making under uncertainties; and advances in software engineering. The new ECMWF strategy identified three pillars to sustain its future development (ECMWF, 2021a): science and technology (world-leading weather and Earth system science, cutting-edge technology and computational science), impact (high-quality products fit for purpose, efficient and easy access to products), and people (inspiring and hiring the best experts). Progress in all these areas will need enhanced collaboration with member states and partners across Europe and beyond.

Observation impact metrics in NWP: A theoretical study. Part I: Optimal systems

AbstractTwo methods are widely used to assess the impact of observations in global numerical weather prediction (NWP): data denial experiments (DDEs) and the forecast sensitivity‐based observation impact (FSOI) method. A DDE measures the impact on forecast accuracy of removing an observation type from the system, whereas FSOI measures the amount by which an observation type reduces the short‐range forecast error (within a system containing all observation types). It is usually found, in global NWP experiments in which both methods are used, that the FSOI impact of a given observation type is greater than its DDE impact. The aim of this article is to present the theory behind the DDE and FSOI metrics that are commonly used and to explore factors that cause DDE and FSOI metrics to give different results. The general theory is presented for an optimal analysis and forecast system and then applied to a simple model with two state variables. To explain the commonly found result of FSOI impact greater than DDE impact, the following system properties are found to be important: mixing of information (and error) by the forecast model between state variables, the rate of forecast error growth, the presence of forecast model error, the way in which observational information is distributed between state variables and denied from them, and the presence of error in the data used for forecast verification. These results provide insight into why NWP systems are resilient to the removal of observational information; they are shown to be resilient if information is denied primarily from well‐observed variables but not when denied primarily from poorly observed variables.

Local temperature forecasts based on statistical post‐processing of numerical weather prediction data

AbstractSix adaptive, short‐term post‐processing methods for correcting systematic errors in numerical weather prediction (NWP) forecasts of near‐surface air temperatures using local meteorological observations are assessed and compared. The methods tested are based on the simple moving average and the more advanced Kalman filter. Forecasts from the rather coarse‐resolution global NWP model Global Forecast System (GFS) and the regional high‐resolution NWP model HARMONIE are post‐processed, and the results are evaluated for 100 private weather stations in Denmark. The performance of the post‐processing methods differs depending on the NWP model. Overall, the combined moving average and a so‐called lead time Kalman filter performs the best. The moving average was shown to be superior to a diurnal bias correction Kalman filter at removing the longer‐term systematic errors for HARMONIE forecast data and comparable for GFS forecast data. Subsequent application of the lead time Kalman filter corrects for the short‐term errors using the real‐time forecast error. The post‐processing method is adaptive and there is no need for a long record of observations or a historical archive of forecasts.

Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation

AbstractThe ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.

Characterizing the Occurrence and Spatial Heterogeneity of Liquid, Ice, and Mixed Phase Low‐Level Clouds Over the Southern Ocean Using in Situ Observations Acquired During SOCRATES

AbstractSupercooled liquid water (SLW) and mixed phase clouds containing SLW and ice over the Southern Ocean (SO) are poorly represented in global climate and numerical weather prediction models. Observed SLW exists at lower temperatures than threshold values used to characterize its detrainment from convection in model parameterizations, and processes controlling its formation and removal are poorly understood. High‐resolution observations are needed to better characterize SLW over the SO. This study characterizes the frequency and spatial distribution of different cloud phases (liquid, ice, and mixed) using in situ observations acquired during the Southern Ocean Clouds, Radiation, Aerosol Transport Experiment Study. Cloud particle phase is identified using multiple cloud probes. Results show occurrence frequencies of liquid phase samples up to 70% between −20°C and 0°C and of ice phase samples up to 10% between −5°C and 0°C. Cloud phase spatial heterogeneity is determined by relating the total number of 1 s samples from a given cloud to the number of segments whose neighboring samples are the same phase. Mixed phase conditions are the most spatially heterogeneous from −20°C to 0°C, whereas liquid phase conditions from −10°C to 0°C and ice phase conditions from −20°C to −10°C are the least spatially heterogeneous. Greater spatial heterogeneity is associated with broader distributions of vertical velocity. Decreasing droplet concentrations and increasing number‐weighted mean liquid diameters occur within mixed phase clouds as the liquid water fraction decreases, possibly suggesting preferential evaporation of smaller drops during the Wegener‐Bergeron‐Findeisen process.

Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models

Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System (GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 °C in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol–cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct “observational” evidence of aerosol’s impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.

Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System

AbstractThe FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Retrieval of cloud-cleared radiances using numerical weather prediction model-analysis and forecast fields for INSAT-3D sounder longwave window channel observations

Estimation of cloud-cleared radiances, without utilizing any supplementary sensors for INSAT-3D Sounder InfraRed window observations is presented here. This method utilizes simulated clear sky radiances by radiative transfer for TOVS (RTTOV) tool, with the help of global numerical weather prediction (NWP) models analysis and forecast fields, unlike the previously reported approaches where observations from auxiliary/collocated sensors are used. The validation of these derived cloud-cleared radiances is carried out with the already existing operational Level 2 (L2) cloud-cleared radiances of Atmospheric Infra-Red Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI), where the retrieval is carried out utilizing auxiliary Microwave Sounders. Also, a comparative study with the optimal cloud-clearing technique has been carried out here, where the retrieval is done by utilizing the clear sky radiances by means of collocated high-resolution InfraRed observations with the help of auxiliary sensors. The results indicate the scope and promising potential of this technique towards the future operational endeavor of deriving the cloud-cleared radiances without any assistance of co-existing auxiliary hardware system.

Binary interaction of typhoons Soulik and Cimaron in 2018 – Part Ⅰ: Observational characteristics and forecast error

To understand structural changes and forecast error, a case study of binary typhoons in the western North Pacific (WNP) of 2018 was investigated using best track and reanalysis data. Soulik was generated on August 16 and Cimaron was generated on August 18, respectively. The 19th typhoon Soulik and 20th typhoon Cimaron co-existed from August 18 to 24 and approached each other. Soulik was located on the western side and Cimaron was located on the eastern side of the WNP. They were located approximately 1300 km from each other at 00 UTC August 22. The Soulik structure began changing around August 22 and became weak and slow, while Cimaron maintained its intensity, size, and moving speed. This observational evidence is likely caused by the binary interaction between two typhoons within a certain distance and environmental steering flow, such as the location of the North Pacific high and strong jet stream of the northern flank of the North Pacific high.Soulik was initially forecasted to make landfall and reach Seoul; however, its track changed from northward to northeastward from August 21 to 23 according to both official guidance and unified model (UM). Four global numerical weather prediction models forecasted different tracks of Soulik. UM and JGSM forecasted a northward track whereas ECMWF and GFS showed a northeastward track for 12 UTC August 21 through 12 UTC August 24. The latter models were similar to the best track. The track forecast error and spread of Soulik were larger than those of Cimaron. The mean absolute error of the maximum wind speed of Soulik was similar to the average of total typhoons in 2018.

Development of objective forecast guidance on tropical cyclone rapid intensity change

AbstractForecasting tropical cyclone (TC) intensity and rapid intensification (RI) are major challenges for numerical weather prediction models. In the present study, a model post‐processing technique, namely the TC intensity guidance on rapid intensification (TINT‐RI), has been developed for the western North Pacific basin (South China Sea excluded). It combines logistic regression and the naïve Bayes classifier to provide RI forecasts up to the next 48 hr. Predictors describing physical and environmental conditions are taken from the outputs of the European Centre for Medium‐Range Weather Forecasts model and the National Oceanic and Atmospheric Administration tropical cyclone heat potential field. Several observed TC characteristics such as persistence (change of past 12 hr intensity) are also included in the algorithm. To further reduce the false alarm ratio, the TINT‐RI makes reference to the intensity forecasts from a previously developed statistical‐dynamical TC intensity forecast model and the change of model vertical wind shear. Verification for TCs with RI reveals that the TINT‐RI is significantly more skilful than the direct model outputs from major global numerical weather prediction models and climatology. A case study on extreme RI of Super Typhoon Hato (1713) during its substantial intensification over the Luzon Strait before entering the northern part of the South China Sea is presented to discuss the performance of the TINT‐RI. Potential ways to improve the TINT‐RI such as using sea surface temperature and its anomaly as the oceanic predictor are discussed.

Calibration of High-Impact Short-Range Quantitative Precipitation Forecast through Frequency-Matching Techniques

Short-lived and intense rainfall is common in Hong Kong during the wet season and often causes disruption to daily lives. As global numerical weather prediction (NWP) models are progressively improved, representation of sub-synoptic or mesoscale systems associated with intense rainfall is better parametrized and resolved than ever before, but their quantitative precipitation forecasts (QPFs) still tend to underestimate the magnitude of intense rainfall. This study calibrated model QPFs over the region of Hong Kong by two frequency-matching methods. In both methods, conversion schemes between the direct model output (DMO) and calibrated forecasts were first established by matching the cumulative distribution to that of the observed data. The “Adaptive Table” method updated the conversion scheme whenever the latest observation fell out of its expected range in the existing scheme, whereas the “Sliding Window” method reconstructed the conversion scheme using data from the most recent two years. The calibration methods had been verified against actual rainfall events with different thresholds, and it was found that both methods could improve model performance for moderate and heavy rainfall in short-range forecasts with similar effectiveness. They were also able to reduce the systematic bias of precipitation forecasts for significant rainfall throughout the verification period.

The Impact of COVID-19 on Weather Forecasts: A Balanced View.

Aircraft reports are an important source of information for numerical weather prediction (NWP). From March 2020, the COVID‐19 pandemic resulted in a large loss of aircraft data but despite this it is difficult to see any evidence of significant degradation in the forecast skill of global NWP systems. This apparent discrepancy is partly because forecast skill is very variable, showing both day‐to‐day noise and lower frequency dependence on the mean state of the atmosphere. The definitive way to cleanly assess aircraft impact is using a data denial experiment, which shows that the largest impact is in the upper troposphere. The method used by Chen (2020, https://doi.org/10.1029/2020gl088613) to estimate the impact of COVID‐19 is oversimplistic. Chen understates the huge importance of satellite data for modern weather forecasts and raises more alarm than necessary about a drop in forecast accuracy.

Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.