Search NASA⌕ Search

SEARCH · Search NASA

Results for “Snow model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment −PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

forecasting↗
NASA NTRS1988

A vertically integrated snow/ice model over land/sea for climate models. I - Development. II - Impact on orbital change experiments

A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.

Neeman, Binyamin U.↗
NASA NTRS1975

A multidisciplinary study of earth resources imagery of Australia, Antarctica and Papua, New Guinea

The author has identified the following significant results. A thirteen category recognition map was prepared, showing forest, water, grassland, and exposed rock types. Preliminary assessment of classification accuracies showed that water, forest, meadow, and Niobrara shale were the most accurately mapped classes. Unsatisfactory results, were obtained in an attempt to discrimate sparse forest cover over different substrates. As base elevation varied from 7,000 to 13,000 ft, with an atmospheric visibility of 48 km, no changes in water and forest recognition were observed. Granodiorite recognition accuracy decreased monotonically as base elevation increased, even though the training set location was at 10,000 ft elevation. For snow varying in base elevation from 9400 to 8420 ft, recognition decreases from 99% at the 9400 ft training set elevation to 88% at 8420 ft. Calculations of expected radiance at the ERTS sensor from snow reflectance measured at the site and from Turner model calculations of irradiance, transmission and path radiance, reveal that snow signals should not be clipped, assuming that calculations and ERTS calibration constants were correct.

Fisher, N. H.↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
NASA NTRS1981

The albedo of snow for partially cloudy skies

The albedo of snow is defined as the ratio of reflected to incident solar energy, and it is an important parameter in the earth's radiation budget analysis and in the study of snowpack's thermal conditions. An approximate model for calculating the incident spectral flux for partially cloudy skies is presented. The input parameters for the calculation are atmospheric precipitable water, turbidity, ozone content, surface pressure, the optical thickness of clouds, and the grain size of snow crystals. The spectral snow reflectance model considers both specular surface reflection and volumetric multiple scattering. The surface reflection is calculated by using a crystal-shape-dependent bidirectional reflectance distribution function; the volumetric multiple scattering is calculated by using a crystal-size-dependent approximate solution in the radiative transfer equation. The model yields spectral and integrated solar flux and snow reflectance as a function of solar elevation and fractional cloud-cover. The illustrative insolation and albedo values were obtained from spectral reflectance and incident flux for representative parameters of Antarctic coastal regions. A simple relationship between grain size and the overcast albedo was obtained. For a set of grain size and shape, the albedo as a function of solar elevation and fractional cloud cover was tabulated.

Choudhury, B. J.↗
NASA NTRS1997

Microphysical Retrievals Over Stratiform Rain Using Measurements from an Airborne Dual-Wavelength Radar-Radiometer

The need to understand the complementarity of the radar and radiometer is important not only to the Tropical Rain Measuring Mission (TRMM) program but to a growing number of multi-instrumented airborne experiment that combine single or dual-frequency radars with multichannel radiometers. The method of analysis used in this study begins with the derivation of dual-wavelength radar equations for the estimation of a two-parameter drop size distribution (DSD). Defining a "storm model" as the set of parameters that characterize snow density, cloud water, water vapor, and features of the melting layer, then to each storm model there will usually correspond a set of range-profiled drop size distributions that are approximate solutions of the radar equations. To test these solutions, a radiative transfer model is used to compute the brightness temperatures for the radiometric frequencies of interest. A storm model or class of storm models is considered optimum if it provides the best reproduction of the radar and radiometer measurements. Tests of the method are made for stratiform rain using simulated storm models as well as measured airborne data. Preliminary results show that the best correspondence between the measured and estimated radar profiles usually can be obtained by using a moderate snow density (0.1-0.2 g/cu cm), the Maxwell-Garnett mixing formula for partially melted hydrometeors (water matrix with snow inclusions), and low to moderate values of the integrated cloud liquid water (less than 1 kg/sq m). The storm-model parameters that yield the best reproductions of the measured radar reflectivity factors also provide brightness temperatures at 10 GHz that agree well with the measurements. On the other hand, the correspondence between the measured and modeled values usually worsens in going to the higher frequency channels at 19 and 34 GHz. In searching for possible reasons for the discrepancies, It is found that changes in the DSD parameter Mu, the radar constants, or the path-integrated attenuation can affect the high frequency channels significantly. In particular, parameters that cause only modest increases in the median mass diameter of the snow, and which have a minor effect on the radar returns or the low frequency brightness temperature, can produce a strong cooling of the 34 GHz brightness temperature.

Meneghini, Robert↗
NASA NTRS2010

HIMALA: Climate Impacts on Glaciers, Snow, and Hydrology in the Himalayan Region

Glaciers are the largest reservoir of freshwater on Earth, supporting one third of the world s population. The Himalaya possess one of the largest resources of snow and ice, which act as a freshwater reservoir for more than 1.3 billion people. This article describes a new project called HIMALA, which focuses on utilizing satellite-based products for better understanding of hydrological processes of the river basins of the region. With support from the US Agency for International Development (USAID), the International Centre for Integrated Mountain Development (ICIMOD), together with its partners and member countries, has been working on the application of satellite-based rainfall estimates for flood prediction. The US National Aeronautics and Space Administration (NASA) partners are working with ICIMOD to incorporate snowmelt and glacier melt into a widely used hydrological model. Thus, through improved modeling of the contribution of snow and ice meltwater to river flow in the region, the HIMALA project will improve the ability of ICIMOD and its partners to understand the impact of weather and climate on floods, droughts, and other water- and climate-induced natural hazards in the Himalayan region in Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan.

Brown, Molly Elizabeth↗
NASA NTRS1975

A comparison of operational and LANDSAT-aided snow water content estimation systems

How LANDSAT imagery can be cost effectively employed to augment an operational hydrologic model is described. Attention is directed toward the estimation of snow water content, a major predictor variable in the volumetric runoff forecasting model. A stratified double sampling scheme is supplemented with qualitative and quantitative analyses of existing operations to develop a comparison between the existing and satellite-aided approaches to snow water content estimation. Results show a decided advantage for the LANDSAT-aided approach.

Sharp, J. M.↗
NASA NTRS1973

Break-up characteristics of Chena River Basin

The author has identified the following significant results. Results from snowmelt model were obtained showing the decreasing changes in snow depth for four elevational ranges in the Chena River Basin. This model is based on the energy balance of the snowpack and the results are shown on attached figures. Also, the measured hydrographs for two stations on the Chena River are shown. The next phase of the study will be to analyze the model using both ERTS-1 imagery and measured field values.

Carlson, R. F.↗
NASA NTRS2024

Sentinel-1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps

Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel-1 radar satellite to map snow depth (SD) at sub-kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah-Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA-2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel-1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel-1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general.

Isis Brangers↗
NASA NTRS2017

Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/ upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

land surface models↗
NASA NTRS2024

Northern Colorado Water Resources: Using Earth Observations to Assess Relationships between Snowpack and Wildfires for Water Utility Management

In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.

airborne LiDAR↗
NASA NTRS2011

Coupled Aerosol-Chemistry-Climate Twentieth-Century Transient Model Investigation: Trends in Short-Lived Species and Climate Responses

The authors simulate transient twentieth-century climate in the Goddard Institute for Space Studies (GISS) GCM, with aerosol and ozone chemistry fully coupled to one another and to climate including a full dynamic ocean. Aerosols include sulfate, black carbon (BC), organic carbon, nitrate, sea salt, and dust. Direct and BC snow-albedo radiative effects are included. Model BC and sulfur trends agree fairly well with records from Greenland and European ice cores and with sulfur deposition in North America; however, the model underestimates the sulfur decline at the end of the century in Greenland. Global BC effects peak early in the century (1940s); afterward the BC effects decrease at high latitudes of the Northern Hemisphere but continue to increase at lower latitudes. The largest increase in aerosol optical depth occurs in the middle of the century (1940s-80s) when sulfate forcing peaks and causes global dimming. After this, aerosols decrease in eastern North America and northern Eurasia leading to regional positive forcing changes and brightening. These surface forcing changes have the correct trend but are too weak. Over the century, the net aerosol direct effect is -0.41 Watts per square meter, the BC-albedo effect is -0.02 Watts per square meter, and the net ozone forcing is +0.24 Watts per square meter. The model polar stratospheric ozone depletion develops, beginning in the 1970s. Concurrently, the sea salt load and negative radiative flux increase over the oceans around Antarctica. Net warming over the century is modeled fairly well; however, the model fails to capture the dynamics of the observedmidcentury cooling followed by the late century warming.Over the century, 20% of Arctic warming and snow ice cover loss is attributed to the BC albedo effect. However, the decrease in this effect at the end of the century contributes to Arctic cooling. To test the climate responses to sulfate and BC pollution, two experiments were branched from 1970 that removed all pollution sulfate or BC. Averaged over 1970-2000, the respective radiative forcings relative to the full experiment were +0.3 and -0.3 Watts per square meter; the average surface air temperature changes were +0.2 degrees and -0.03 C. The small impact of BC reduction on surface temperature resulted from reduced stability and loss of low-level clouds.

Koch, Dorothy↗
NASA NTRS1975

An integrated study of earth resources in the state of California using remote sensing techniques

The author has identified the following significant results. A weighted stratified double sample design using hardcopy LANDSAT-1 and ground data was utilized in developmental studies for snow water content estimation. Study results gave a correlation coefficient of 0.80 between LANDSAT sample units estimates of snow water content and ground subsamples. A basin snow water content estimate allowable error was given as 1.00 percent at the 99 percent confidence level with the same budget level utilized in conventional snow surveys. Several evapotranspiration estimation models were selected for efficient application at each level of data to be sampled. An area estimation procedure for impervious surface types of differing impermeability adjacent to stream channels was developed. This technique employs a double sample of 1:125,000 color infrared hightflight transparency data with ground or large scale photography.

Colwell, R. N.↗
NASA NTRS2022

Overview of the MOSAiC Expedition - Snow and Sea Ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the five MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties, and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in-situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models, and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

snow and sea ice↗
NASA NTRS2010

Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme

As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.

Molthan, Andrew L.↗
NASA NTRS2011

Magnitude and Seasonality of Wetland Methane Emissions from the Hudson Bay Lowlands (Canada)

The Hudson Bay Lowlands (HBL) is the second largest boreal wetland ecosystem in the world and an important natural source of global atmospheric methane. We quantify the HBL methane emissions by using the GEOS-Chem chemical transport model to simulate aircraft measurements over the HBL from the ARCTAS and pre-HIPPO campaigns in May-July 2008, together with continuous 2004-2008 surface observations at Fraserdale (southern edge of HBL) and Alert (Arctic background). The difference in methane concentrations between Fraserdale and Alert is shown to be a good indicator of HBL emissions, and implies a sharp seasonal onset of emissions in late May (consistent with the aircraft data), a peak in July-August, and a seasonal shut-off in September. The model, in which seasonal variation of emission is mainly driven by surface temperature, reproduces well the observations in summer but its seasonal shoulders are too broad. We suggest that this reflects the suppression of emissions by snow cover and greatly improve the model simulation by accounting for this effect. Our resulting best estimate for HBL methane emissions is 2.3 Tg/a, several-fold higher than previous estimates (Roulet et al., 1994; Worthy et al., 2000).

Pickett-Heaps, C. A.↗
NASA NTRS2009

Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 Model for a Winter Storm

Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations. This study employs this method to evaluate the accuracy of the simulated radiative properties by the MM5 model with different microphysical schemes. It is found that the representations of particle density, size, and mass in the different schemes in the MM5 model determine the model s performance when predicting a winter storm over the eastern Pacific Ocean. Schemes lacking moderate density particles (i.e. graupel), with snow flakes that are too large, or with excessive mass of snow or graupel lead to degraded prediction of the radiative properties as observed by the TRMM satellite. This study demonstrates the uniqueness of the combination of both an active microwave sensor (PR) and passive microwave sensor (TMI) onboard TRMM on assessing the accuracy of numerical weather forecasting. It improves our understanding of the physical and radiative properties of different types of precipitation particles and provides suggestions for better representation of cloud and precipitation processes in numerical models. It would, ultimately, contribute to answering questions like "Why did it not rain when the forecast says it would?"

Han, Mei↗