scholarly journals Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 551
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Jiajia Mao ◽  
Bai Li ◽  
Chunwei Guo ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Thermal Conditions ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Multi Scale ◽  
Quantitative Precipitation Forecast ◽  
Radar Echo ◽  
Multiple Cycle ◽  
Microwave Radiometers ◽  
The Impact

In this study, the temperature and relative humidity profiles retrieved from five ground-based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall performed best by assimilating the ground-based microwave radiometer profiles.

scholarly journals Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 74
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Bai Li ◽  
Jiajia Mao ◽  
Dawei Lin
Keyword(s):  
Control Experiment ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Radar Data ◽  
Specific Humidity ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Microwave Radiometers ◽  
The Impact ◽  
Precipitation Enhancement

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2015 ◽  
Vol 8 (10) ◽  
pp. 10755-10792
Author(s):  
A. M. Dzambo ◽  
D. D. Turner ◽  
E. J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km MSL), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, mid-latitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RH are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

scholarly journals Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological cycle in the Mediterranean Experiment

2021 ◽  
Vol 21 (1) ◽  
pp. 463-480
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont
Keyword(s):  
Hydrological Cycle ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Scale Model ◽  
Data Set ◽  
The Mediterranean ◽  
The Impact ◽  
Forecast Quality ◽  
Observation Period ◽  
Wind Profilers

Abstract. This study was performed in the framework of HyMeX (Hydrological cycle in the Mediterranean Experiment), which aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation period of the field campaign (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow were assimilated. Such observations are important for the description of the feeding of the convective mesoscale systems with humidity (Duffourg and Ducrocq, 2011; Bresson et al., 2012; Ricard et al., 2012). Among them there were a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data. The aim of the paper is to assess the impact of the assimilation of these four observation types on the analyses and the forecasts from the 3 h forecast range (first guess) up to the 48 h forecast range. In order to assess this impact, several observing system experiments (OSEs) or so-called denial experiments, were carried out by removing one single data set from the observation data set assimilated in the reanalysis. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but this impact is not statistically significant. The assimilation of the Spanish radar data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains located over Spain. Moreover, marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data, as they represent a very small data set, mainly located over the sea.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016 ◽  
Vol 9 (4) ◽  
pp. 1613-1626 ◽  
Author(s):  
Andrew M. Dzambo ◽  
David D. Turner ◽  
Eli J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

scholarly journals An Ice-Phase Microphysics Forward Model and Preliminary Results of Polarimetric Radar Data Assimilation

2017 ◽  
Vol 145 (2) ◽  
pp. 683-708 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski ◽  
Derek Posselt
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Weather Prediction ◽  
Radar Data ◽  
Forward Model ◽  
Precipitation Forecast ◽  
Upper Troposphere ◽  
The Impact ◽  
Radar Data Assimilation ◽  
Ice Phase

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

scholarly journals Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System

2016 ◽  
Vol 31 (3) ◽  
pp. 957-983 ◽  
Author(s):  
Nusrat Yussouf ◽  
John S. Kain ◽  
Adam J. Clark
Keyword(s):  
Flash Flood ◽  
Stage Iv ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Flood Event ◽  
Forecast Period ◽  
Low Level ◽  
Probabilistic Forecasts ◽  
The Impact

Abstract A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.

scholarly journals The application of LEPS technique for Quantitative Precipitation Forecast (QPF) in Southern Italy

2006 ◽  
Vol 7 ◽  
pp. 1-8 ◽  
Author(s):  
S. Federico ◽  
E. Avolio ◽  
C. Bellecci ◽  
M. Colacino
Keyword(s):  
Case Studies ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Computational Time ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Subjective Analysis ◽  
Quantitative Precipitation Forecast ◽  
The Impact

Abstract. This paper reports preliminary results of a Limited area model Ensemble Prediction System (LEPS), based on RAMS, for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time, in order to implement LEPS operational, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that forms the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12 km horizontal resolution. Hereafter this ensemble will be referred also as LEPS_12L30. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecast, LEPS_12L30 forecasts are compared to a lower resolution ensemble, based on RAMS that has 50 km horizontal resolution and 51 members, nested in each ECMWF-EPS member. Hereafter this ensemble will be also referred as LEPS_50L30. LEPS_12L30 and LEPS_50L30 results were compared subjectively for all case studies but, for brevity, results are reported for two "representative" cases only. Subjective analysis is based on ensemble-mean precipitation and probability maps. Moreover, a short summary of objective scores. Maps and scores are evaluated against reports of Calabria regional raingauges network. Results show better LEPS_12L30 performance compared to LEPS_50L30. This is obtained for all case studies selected and strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria, at least for set-ups and case studies selected in this work.

scholarly journals Impact of Rainfall Assimilation on High-Resolution Hydrometeorological Forecasts over Liguria, Italy

2017 ◽  
Vol 18 (10) ◽  
pp. 2659-2680 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Thomas Gastaldo
Keyword(s):  
High Resolution ◽  
Precipitation Forecast ◽  
Test Bed ◽  
Structure Amplitude ◽  
Meteorological Model ◽  
Quantitative Precipitation Forecast ◽  
Verification Methodology ◽  
The Impact ◽  
Assimilation Scheme ◽  
Humidity Profiles

Abstract The autumn of 2014 was characterized by a number of severe weather episodes over Liguria (northern Italy) associated with floods and remarkable damage. This period is selected as a test bed to evaluate the performance of a rainfall assimilation scheme based on the nudging of humidity profiles and applied to a convection-permitting meteorological model at high resolution. The impact of the scheme is assessed in terms of quantitative precipitation forecast (QPF) applying an object-oriented verification methodology that evaluates the structure, amplitude, and location (SAL) of the precipitation field, but also in terms of hydrological discharge prediction. To attain this aim, the meteorological model is coupled with the operational hydrological forecasting chain of the Ligurian Hydrometeorological Functional Centre, and the whole system is implemented taking operational requirements into account. The impact of rainfall data assimilation is large during the assimilation period and still relevant in the following 3 h of the free forecasts, but hardly lasts more than 6 h. However, this can improve the hydrological predictions. Moreover, the impact of the assimilation is dependent on the environment characteristics, being more effective when nonequilibrium convection dominates, and thus an accurate prediction of the local triggering for the development of the precipitation system is required.

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