Weather Research And Forecasting Model Forecasts Research Articles

Urban Flood Analysis and Early Warning Solution for Disaster Resilience

The objective of this work is to present the experimental findings of an urban flood early warning system developed by combining a mesoscale numerical weather prediction model (WRF) forecast with a distributed hydrologic modelling system. Hydrodynamic models have been used in the simulation of intricate and interrelated dynamic systems, such as urban watersheds and urban water infrastructure. The use of these models in assessing the flood hazard, vulnerability, and risk is well-established. On the other hand, the Numerical weather prediction (NWP) such as WRF model output are crucial to increase the lead time for the flood forecasting. The Weather Research and Forecasting (WRF) model is employed for the purpose of generating precipitation forecasts, which are subsequently utilized to conduct hydrologic and hydrodynamic simulations. In accordance with the climatology of the study area, the WRF model forecast is juxtaposed with the observed rainfall data for days 1, 2, and 3. Statistical indicators of accuracy assessment suggest acceptable degree of correlation with the observed precipitation. The experiment successfully designed and implemented a comprehensive web-based urban flood forecast and early warning system. The system is able to predict possible inundation areas within the catchment due to projected rainfall. Additionally, the study develops a web-based Flood Management Information System (MIS) that is fully automated and disseminate early warning with a lead time of 72 hours.

A Polarimetric Radar Operator and Application for Convective Storm Simulation

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere.

Prediction of Heatwave 2013 over Andhra Pradesh and Telangana, India using WRF Model

Heatwaves are acknowledged to be the major meteorological disaster, causing a noticeable impact on humans and animals’ lives during the last few decades. The number, frequency, duration, intensity, and areal extent of the heatwaves are on the rise during recent years. The Maximum temperature data of 2013 is analyzed to assess the synoptic nature, intensity, frequency, and various significant facets of the heatwave over the south peninsular states of Andhra Pradesh and Telangana. Indian subcontinent experienced a major heatwave during 2013, which claimed 1216 human lives. Even though the highest intensity of maximum temperatures is observed in May over major areas of India, the increasing (incidence, duration, number of spells, and the sweltering temperatures) number of heatwaves are observed over many parts of the country. The northwest and southeast coastal regions are the two heat wave prone regions. The advection of heat from the northwest with the aid of north-westerly winds causes heatwaves over northwest India to sweep or move towards India’s southeast and east coast. The heatwave record over south-eastern India, i.e, Andhra Pradesh and the adjoining Telangana state during May 22–24, 2013 were described in this study. Maximum temperatures above 40°C are observed with a sudden rise by 6 to 7°C over the study region. An attempt is made to predict the maximum temperatures 72 hours before the existence of a heatwave at 3 km horizontal resolution using the Advanced core of the Weather Research and Forecasting (WRF) model. Model predicted temperature values match with observations and the statistical metrics show a high index of the agreement, lower values for root-mean-square error and mean absolute error. Atmospheric circulation patterns associated with this heatwave are also presented. The arrest of sea breeze, the hovering of diabatic heat because of subsidence is the factor that abetted the heatwave blockade over the south-eastern part of the country. The WRF model forecasts could present the occurance of the heat wave over AP and Telangana region with 72 hour lead time with high accuracy.

Sensitivity of WRF-derived hydrometeorological extremes to sea surface temperatures in regions with complex topography and diverse climate

This study investigates the impact of sea surface temperature (SST) describing the lower boundary of Weather Research and Forecasting (WRF) model on the extreme weather events that occurred on the Mediterranean (MED) and the Eastern Black Sea (EBS) regions of Turkey. For each region, one extreme event case is selected and characterized as a summer convective system in EBS and as a winter synoptic system in MED region. The SST impact on the WRF model forecasts of these events is accomplished in two ways. First, the SST analysis is conducted by making a 10-days simulation for each event with and without activating the SST options (update and skin options) available in the model configuration. In these runs, the selected initial and boundary condition datasets, Global Forecasting System (GFS) for the MED region and the ERA5 for the EBS regions, provide corresponding internal SST data. Second, the prediction performance of the model is evaluated among the simulations of non-external, time-varying (GFS and ERA5 SST) and external, time–varying, high-resolution SST products (The Group for High Resolution Sea Surface Temperature; GHRSST, Medspiration, and NCEP) for each event. The results show that the WRF model simulations, even for short-term event predictions, are highly sensitive to time-variant SST options. The overestimation and high spreading feature of maximum and total precipitation are realistically reduced by time-variant SST products. The Medspiration and the NCEP sources for the MED region and the GHRSST and the Medspiration sources for the EBS region yielded warmer SSTs among the simulations. By establishing the amplified air-sea interactions, these sources provide more appropriate peak precipitation distributions in both regions. Furthermore, based on this study, the effect of using high-resolution SST (GHRSST and Medspiration) is more prominent in predicting convective event developed over complex topography of the EBS while the resolution effect is not much critical for synoptic system simulation even under the complex topography in MED. Lastly, the highest mean correlation of conditional hourly rain is calculated as 0.65 with the Medspiration product on MED and 0.48 with the GHRSST product on the EBS region.

Multi‐resolution incremental 4D‐Var for WRF: Implementation and application at convective scale

AbstractThe current four‐dimensional variational (4D‐Var) data assimilation (DA) algorithm, available in the community Weather Research and Forecasting (WRF) model's DA (WRFDA) system, can run only at the same resolution as that of the WRF model forecast, which makes it computationally prohibitive for operational applications at convective scale. The Multi‐Resolution Incremental 4D‐Var (MRI‐4DVar) has been developed in this study in order to speed up WRF 4D‐Var through a three‐stage procedure in each outer loop. One key aspect of WRF MRI‐4DVar is the introduction of the inverse control variable transform within WRFDA that allows the proper resolution change between different outer loops for the control variables projected in the vertical empirical orthogonal function (EOF) space. MRI‐4DVar's computational efficiency and forecast performance are demonstrated by applying it to an afternoon thunderstorm event over northern Taiwan with a 2 km model resolution setting. Comparing to the full‐resolution 4D‐Var experiment, two MRI‐4DVar configurations with a speed‐up of 4.5 and 7.5 times performed similarly well in terms of Fractions Skill Score (FSS) of 6‐hr accumulated rainfall forecasts and hourly variation of total rainfall amount, indicating MRI‐4DVar's potential for operational applications at convective scale.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

The Use of Moist Potential Vorticity Vector as Diagnostic Variable of Rainfall Events in Tanzania

The work of this paper is a first step of the new paradigm, to use the Moist Potential Vorticity Vector (MPVV) as a diagnostic variable of rainfall events in Tanzania. The paper aims at computing and assessing the usefulness of MPVV in the diagnosis of rainfall events that occurred on 08th and 09th May 2017 over different regions in Tanzania. The relative contributions of horizontal, vertical components and the magnitude of MPVV on diagnosis of rainfall events are assessed. Hourly dynamic and thermodynamic variables of wind speed, temperature, atmospheric pressure and relative humidity from the numerical output generated by the Weather Research and Forecasting (WRF) Model, running at Tanzania Meteorological Agency (TMA) are used in computation of MPVV. The computed MPVV is then compared with WRF model forecasts and observed rainfall. It is found that in most parts of the country, particularly over coastal areas and North-Eastern Highlands, MPVV exhibited positive values in the lower troposphere (925hPa) and (850hPa) indicating local instability possibly associated with topographic effects, and continent/ocean contrast. MPVV is mostly positive with slightly negative values indicating instabilities (due to possible convective instability). Moreover, MPVV provides remarkably accurate tracking of the locations received rainfall, suggesting its potential use as a dynamic diagnostic variable of rainfall events in Tanzania.

Dynamical Downscaling of SINTEX-F2v CGCM Seasonal Retrospective Austral Summer Forecasts over Australia

An ensemble of 1-month-lead seasonal retrospective forecasts generated by the Scale Interaction Experiment (SINTEX)–Frontier Research Center for Global Change (FRCGC), version 2 tuned for performance on a vector supercomputer (SINTEX-F2v), coupled global circulation model (CGCM) were downscaled using the Weather Research and Forecasting (WRF) Model to improve the forecast of the austral summer precipitation and 2-m air temperatures over Australia. A set of four experiments was carried out with the WRF Model to improve the forecasts. The first was to drive the WRF Model with the SINTEX-F2v output, and the second was to bias correct the mean component of the SINTEX-F2v forecast and drive the WRF Model with the corrected fields. The other experiments were to use the SINTEX-F2v forecasts and the mean bias-corrected SINTEX-F2v forecasts to drive the WRF Model coupled to a simple mixed layer ocean model. Evaluation of the forecasts revealed the WRF Model driven by bias-corrected SINTEX-F2v forecasts to have a better spatial and temporal representation of forecast precipitation and 2-m air temperature, compared to SINTEX-F2v forecasts. Using a regional coupled model with the bias-corrected SINTEX-F2v forecast as the driver further improved the skill of the precipitation forecasts. The improvement in the WRF Model forecasts is due to better representation of the variables in the bias-corrected SINTEX-F2v forecasts driving the WRF Model. The study brings out the importance of including air–sea interactions and correcting the global forecasts for systematic biases before downscaling them for societal applications over Australia. These results are important for potentially improving austral summer seasonal forecasts over Australia.

An integrated wind-forecast system based on the weather research and forecasting model, Kalman filter, and data assimilation with nacelle-wind observation

To forecast wind speed at hub-height is a challenging task for wind energy application, especially in Japan where the terrain feature is very complex and large fluctuations are observed in surface wind field. In this study, an integrated system to predict the hub-height wind speed has been developed by combining data assimilation and Kalman filter with the high resolution Weather Research and Forecasting (WRF) model. Assimilating the nacelle wind data (quality-controlled) and the Kalman filter algorithm effectively improves accuracy of the WRF model forecast by optimizing initial condition and post-processing the model output, respectively. It is found that the WRF model forecasts can be markedly improved after assimilating the nacelle wind data through the Gridpoint Statistical Interpolation analysis system, with the relative improvements of 34.3%, 23.9%, and 8.8% in ME (mean error), RMSE (root mean square error), and IA (index of agreement), respectively. The implementation of the Kalman filter can significantly reduce ME and RMSE while increases the value of IA as well. Further improvement can be achieved if the Kalman filter and nacelle wind data assimilation are implemented simultaneously. It is observed that the role of the Kalman filter is more dominant for the wind band of rated out speeds, while data assimilation is effective in reducing the random errors and becomes more important in rare or extreme weather conditions. Both data assimilation and Kalman filter modules apply the nacelle wind data which is routinely available, so the system can be easily adopted in different wind farm sites for operational use.

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ∼10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

The impact of background error parameters on heavy rainfall prediction

A heavy rainfall event over the northwest of India is selected to investigate the impact of Atmospheric Infrared Sounder (AIRS)-retrieved temperature and moisture profile assimilation on regional model prediction. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system (WRFDA) is used to assimilate AIRS profiles with tuning of two major background error parameters – viz. length and variance scales. Assimilation of AIRS profiles improves the WRF model analyses, which are closer to the Moderate Resolution Imaging Spectrometer (MODIS) profiles compared to those without assimilation experiment. Results show that within a wide parameter range of length and variance scales, the assimilation of AIRS-retrieved profiles has a positive influence on heavy rainfall prediction. Approximately 9–30, 5–42, and 0.5–3.0% domain average values of improvement are observed after AIRS profile assimilation for different values of length and variance scales in temperature, water vapour mixing ratio, and rainfall prediction, respectively. This study shows that the impact of observations on the WRF model forecast is dependent on the length and variance scale parameters of background error, and lower values of length scale in WRFDA result in degradation of the forecast.

A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

Impact of ECMWF, NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian Region

The global model analysis has significant impact on the mesoscale model forecast as global model provides initial condition (IC) and lateral boundary conditions (LBC) for the mesoscale model. With this objective, four operational global model analyses prepared from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), NCEP Global Forecasting System (GFS), and National Centre for Medium Range Weather Forecasting (NCMRWF) are used daily to generate IC and LBC of the mesoscale model during 13th December 2012 to 13th January 2013. The Weather Research and Forecasting (WRF) model version 3.4, broadly used for short-range weather forecast, is adopted in this study as mesoscale model. After initial comparison of global model analyses with Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles, daily WRF model forecasts initialized from global model analyses are compared with in situ observations and AIRS profiles. Results demonstrated that forecasts initialized from the ECMWF analysis are closer to AIRS-retrieved profiles and in situ observations compared to other global model analyses. No major differences are occurred in the WRF model forecasts when initialized from the NCEP GDAS and GFS analyses, whereas these two analyses have different spatial resolutions and observations used for assimilation. Maximum RMSD is seen in the NCMRWF analysis-based experiments when compared with AIRS-retrieved profiles. The rainfall prediction is also improved when WRF model is initialized from the ECMWF analysis compared to the NCEP and NCMRWF analyses.

Sensitivity of Limited-Area Hybrid Variational-Ensemble Analyses and Forecasts to Ensemble Perturbation Resolution

Abstract Dual-resolution (DR) hybrid variational-ensemble analysis capability was implemented within the community Weather Research and Forecasting (WRF) Model data assimilation (DA) system, which is designed for limited-area applications. The DR hybrid system combines a high-resolution (HR) background, flow-dependent background error covariances (BECs) derived from a low-resolution ensemble, and observations to produce a deterministic HR analysis. As DR systems do not require HR ensembles, they are computationally cheaper than single-resolution (SR) hybrid configurations, where the background and ensemble have equal resolutions. Single-observation tests were performed to document some characteristics of limited-area DR hybrid analyses. Additionally, the DR hybrid system was evaluated within a continuously cycling framework, where new DR hybrid analyses were produced every 6 h over ~3.5 weeks. In the DR configuration presented here, the deterministic backgrounds and analyses had 15-km horizontal grid spacing, but the 32-member WRF Model–based ensembles providing flow-dependent BECs for the hybrid had 45-km horizontal grid spacing. The DR hybrid analyses initialized 72-h WRF Model forecasts that were compared to forecasts initialized by an SR hybrid system where both the ensemble and background had 15-km horizontal grid spacing. The SR and DR hybrid systems were coupled to an ensemble adjustment Kalman filter that updated ensembles each DA cycle. On average, forecasts initialized from 15-km DR and SR hybrid analyses were not statistically significantly different, although tropical cyclone track forecast errors favored the SR-initialized forecasts. Although additional studies over longer time periods and at finer grid spacing are needed to further understand sensitivity to ensemble perturbation resolution, these results suggest users should carefully consider whether SR hybrid systems are worth the extra cost.

Performance of weather research and forecasting model with variable horizontal resolution

In this paper, Weather Research and Forecasting (WRF) model is employed with three different horizontal grid spacings (45, 15 and 5 km) to assess the impact of horizontal resolution on short-range weather forecast. Simulations are carried out daily at 0000 UTC over the Indian region during the entire month of July 2011. A rigorous validation is performed against surface observations, radiosonde measurements and Tropical Rainfall Measuring Mission (TRMM) 3B42 merged rainfall product. Results show that horizontal resolution has a substantial impact on the WRF model forecast, particularly on the near surface temperature, moisture, winds and rainfall forecasts. Relative to 45-km horizontal grid spacing, 24-h forecasts of near surface temperature, moisture and winds are improved by ∼15, 9 and 4 %, respectively, when horizontal grid spacing is reduced to 5 km. Noteworthy improvement is also seen in the 24-h rainfall forecasts of the WRF model as the horizontal grid spacing decreased from 45 to 5 km. Larger improvements are observed over the Western Ghats and northeastern part of India compared to central India, which demonstrate the importance of finer resolution over the mountainous terrain compared to plains.

Evaluation of Weather Research and Forecasting Model Predictions Using Micrometeorological Tower Observations

Here we assess the predictive skill of short-range weather forecasts from the Weather Research and Forecasting (WRF) model with the help of micrometeorological tower observations. WRF model forecasts at a 3-h temporal resolution and 5000-m spatial resolution are compared with ground observations collected at micrometeorological towers during the year 2011 over the Indian landmass. Results show good agreement between the WRF model forecast and tower observed surface temperature and relative humidity, 10-m wind speed, and surface pressure. The WRF model simulations of surface energy fluxes, such as incoming shortwave, longwave radiation, and ground heat flux are also compared with micrometeorological tower measurements. Relatively high errors in incoming shortwave radiation flux may be attributed to the lack of accurate cloud prediction and the non-inclusion of aerosol load. The cyclic pattern of errors in surface relative humidity is found to be tightly and oppositely coupled with the incoming longwave radiation flux. Errors in soil heat fluxes during daytime hours are dominated by errors in the incoming shortwave radiation flux.

Skill of regional and global model forecast over Indian region

The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).

Impact of Kalpana-1 derived land surface albedo on short-range weather forecasting over the Indian subcontinent

The skill of weather forecasts at high spatial resolution depends on accurate representation of land surface states at appropriate spatial and temporal scales that modulate flux partitioning in the numerical weather prediction models. In this study, the Weather Research and Forecasting (WRF) model is customized to assess the impact of land surface albedo (LSA) derived from Kalpana-1 Very High Resolution Radiometer (K1VHRR) in comparison to default monthly climatological albedo from the United States Geological Survey (USGS). A two-stage upscaling of ground-measured albedo from Agro-Met Stations is performed to derive K1VHRR LSA. This combines multispectral reflectance at intermediate scales from the Advanced Wide Field Sensor on board Resourcesat-2 at Low Earth Orbiting platform and the planetary (Earth-atmosphere system) albedo from Kalpana-1 visible band at Geostationary Earth Orbiting platform. Two separate experiments, with real-time K1VHRR LSA and USGS climatological LSA (CNT), are performed to evaluate the impact of real-time K1VHRR LSA on daily WRF model forecasts during July 2009. Additional experiments are performed to assess the impact of real-time and climatological K1VHRR albedo against USGS climatological albedo based experiment. Results show that real-time K1VHRR albedo improves the surface temperature, specific humidity, and wind speed forecasts as compared to CNT experiments. The impact of climatological and real-time K1VHRR LSA is small compared to the advantage of using K1VHRR over USGS. Moreover, real-time K1VHRR albedo has additional benefits to improve the representation of seasonal variability. Results show that the real-time K1VHRR LSA has slight positive impact on rainfall forecast.

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

AbstractRainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four‐dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.

Evaluation of upper tropospheric humidity from NCEP analysis and WRF Model Forecast with Kalpana observation during Indian summer monsoon 2010

AbstractThis paper presents an evaluation of upper tropospheric humidity (UTH) fields from National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis and Weather Research and Forecasting (WRF) mesoscale model forecast with satellite derived UTH. For this purpose, UTH (weighted average of relative humidity in 200–500 hPa layer) obtained from Indian geostationary satellite Kalpana is compared with the UTH computed from NCEP analysis and WRF model forecast during the summer monsoon, 2010. Kaplana‐derived products are not included in the NCEP analysis: therefore, a comparison of the two independent datasets is promising. This study indicates that UTH derived from different sources (Kalpana UTH, NCEP Analysed and subsequent model forecasted UTH) matches very well over the Indian region. However, there are region‐specific small departures in UTH which increases the model predicted UTH. Copyright © 2012 Royal Meteorological Society