Use of Four-Dimensional Data Assimilation by Newtonian Relaxation and Latent-Heat Forcing to Improve a Mesoscale-Model Precipitation Forecast: A Case Study

1988 ◽  
Vol 116 (12) ◽  
pp. 2593-2613 ◽  
Author(s):  
Wei Wang ◽  
Thomas T. Warner
Keyword(s):  
Data Assimilation ◽  
Latent Heat ◽  
Mesoscale Model ◽  
Precipitation Forecast ◽  
Four Dimensional Data Assimilation ◽  
Model Precipitation

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

scholarly journals Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

2018 ◽  
Vol 10 (9) ◽  
pp. 1380 ◽  
Author(s):  
Yanhui Xie ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Youjun Dou ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Observation Data ◽  
Rainfall Events ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact ◽  
Heavy Rainfall Events

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals Remote sensing data assimilation in WRF-UCM mesoscale model: Madrid case study

2010 ◽  
Author(s):  
R. San José ◽  
J. L. Pérez ◽  
J. L. Morant ◽  
R. M. González
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0)

2017 ◽  
Author(s):  
Orren Russell Bullock Jr. ◽  
Hosein Foroutan ◽  
Robert C. Gilliam ◽  
Jerold A. Herwehe
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Global Analysis ◽  
Analysis Data ◽  
Global Scale ◽  
Model Errors ◽  
Near Surface ◽  
Target Values ◽  
Four Dimensional Data Assimilation ◽  
Reference Fields

Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four dimensional data assimilation (FDDA) by the nudging of temperature, humidity and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of analysis nudging developed for the Penn State / NCAR Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its unstructured Voronoi mesh. Reference fields generated from 1° × 1° National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

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