“Non-geodetic” approaches in the analysis of terrestrial CDGPS data for the retrieval of the atmospheric precipitable water at local scale during severe weather phenomena

2014 ◽  
Author(s):  
Tammaro Umberto ◽  
Riccardi Umberto ◽  
Sorrentino Valeria ◽  
Forte Irene ◽  
Capuano Paolo
Keyword(s):  
Precipitable Water ◽  
Local Scale ◽  
Severe Weather

A Case Study of Weather Radar Data Assimilation into the Harmonie Numerical Weather Prediction System

2020 ◽  
Author(s):  
Silas Michaelides ◽  
Serguei Ivanov ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Frontal Zone ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Radar Data ◽  
Severe Weather ◽  
Numerical Weather ◽  
Radar Data Assimilation

<p>Quantitative Precipitation Forecasting (QPF) is among the most central challenges of atmospheric prediction systems. The primary aim of such a task is the generation of accurate estimates of heavy precipitation events associated with severe weather, atmospheric fronts and heavy convective rainfalls. QPF is still among the most intricate challenges of Numerical Weather Prediction. The efforts in this direction are mainly concentrated on improving model formulations for microphysics and convective process and remote sensing data assimilation.</p><p>This paper describes the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model. This task is performed for a case study focusing on a wintertime frontal cyclone over the island of Cyprus. Reflectivity measurements from two weather radars, at Larnaka and Paphos, are exploited for simulations of severe weather conditions associated with this synoptic-scale system. Through the variational assimilation procedure, the model takes into account the atmospheric processes occurring in the upstream flow which can be outside the area of radar measurements. The focus is on the precipitable water vapor content and its changes during the cyclone evolution, as well as on the impact of the radar data assimilation on precipitation estimates.</p><p>The results show that the numerical experiments exhibit, in general, a suitable simulation of precipitable water at different stages of the cyclone. In particular, the bulk of the rainfall volume exhibits three stages: intensive rain on the cyclone's frontal zone, weaker precipitation immediately behind the front, and the secondary enhancement of rainfall. The largest corrections due to RDA are of up to 5 mm and occur during the approach of the cyclone frontal zone in a form of enhanced rainfall over the whole area, but more prominently in weak precipitation locations.</p>

The Impact of Assimilating Satellite-derived Layered Precipitable Water, Cloud Water Path and Radar Data on Short-Range Thunderstorm Forecasts

2021 ◽  
Author(s):  
Sijie Pan ◽  
Jidong Gao ◽  
Thomas A. Jones ◽  
Yunheng Wang ◽  
Xuguang Wang ◽  
...  
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Cloud Water ◽  
Severe Weather ◽  
Radar Observations ◽  
Water Path ◽  
Convective Scale ◽  
Cloud Water Path ◽  
The Impact

AbstractWith the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived Layered Precipitable Water (LPW) and Cloud Water Path (CWP) in addition to NEXRAD radar observations on short-term convective scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components, the GSI-EnKF system, and a deterministic 3DEnVAR system. This study examines deterministic 0-6 h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface, radar and satellite derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

scholarly journals 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

2019 ◽  
Vol 12 (1) ◽  
pp. 345-361 ◽  
Author(s):  
Witold Rohm ◽  
Jakub Guzikowski ◽  
Karina Wilgan ◽  
Maciej Kryza
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Vertical Profile ◽  
Weather Prediction ◽  
Wrf Model ◽  
Precipitable Water ◽  
Severe Weather ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Gnss Data

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.

scholarly journals Splitting of the middle layer of LPW SAFNWC/MSG satellite product in order to improve the monitoring of pre-convective environments

2008 ◽  
Vol 2 (1) ◽  
pp. 71-75
Author(s):  
G. Cuevas ◽  
M. A. Martinez ◽  
M. Velazquez ◽  
J. Ruiz ◽  
M. Manso
Keyword(s):  
Neural Networks ◽  
Precipitable Water ◽  
Middle Layer ◽  
Horizontal Resolution ◽  
Severe Weather ◽  
Convective System ◽  
Weather Event ◽  
Radar Images ◽  
The Impact ◽  
Severe Weather Event

Abstract. Seven of the infrared channels from the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) instrument, on board the Meteosat Second Generation (MSG), are used to retrieve Layer Precipitable Water (LPW) and Stability Analysis Imagery (SAI) in the SAFNWC framework. Both products are retrieved using a statistical retrieval based on neural networks; they are routinely generated every fifteen minutes at a satellite horizontal resolution of 3 km in NADIR only in cloud-free areas. Many factors are involved in the development of severe weather and these parameters are only some of the indicators. However, due to the high resolution of these products, the use of them in conjunction with satellite and radar images can help to identify mesoscale features related to convection. The MSG moisture and parcel instability time trend fields are especially useful during the period previous to convection. Once the outbreak of convection occurs, the products calculated in the clear air pixels surrounding the convective system can give us hints to anticipate its evolution. SAFNWC LPW and SAI were analyzed for a severe weather event during August 2004. A thunderstorm over Teruel (Spain) produced intense precipitation and hail; a tornado developed while this thunderstorm was moving towards SE. The pre-convective parcel potential buoyancy and moisture SAFNWC products changed in a way that was consistent with the observed intense convective activity. In previous studies, the atmospheric moisture in medium levels, which has been proven to be relevant in some cases, was represented by only one level parameter (ML: middle layer LPW). However, it was observed that this layer is too thick to do an adequate analysis of moisture available for convection. Hence, an improvement on the LPW algorithm has been carried out by splitting the middle layer into two new sub-layers (approximately separated at 700 hPa) and training two new neural networks. The impact of monitoring moisture in the new sub-layers separately in this severe weather event has been tested, and the improvements achieved have been evaluated.

scholarly journals 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

2018 ◽  
Author(s):  
Witold Rohm ◽  
Jakub Guzikowski ◽  
Karina Wilgan ◽  
Maciej Kryza
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Severe Weather ◽  
Numerical Weather ◽  
Gnss Data ◽  
The Impact

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the best estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. This paper shows the results of assimilation of GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through WRF Data Assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the Zenith Troposphere Delay (ZTD), Precipitable Water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June of 2013, (2) assimilation of: PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 48 hours. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June 2013) show that the assimilation of GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8 %, and a 20 % improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5–23 May 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48 h forecast) but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24 h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather cases is mixed, one out of three investigated cases shows positive impact of GNSS data, whereas other two neutral or negative.

A Study of the Characteristics and Assimilation of Retrieved MODIS Total Precipitable Water Data in Severe Weather Simulations

2008 ◽  
Vol 136 (9) ◽  
pp. 3608-3628 ◽  
Author(s):  
Shu-Hua Chen ◽  
Zhan Zhao ◽  
Jennifer S. Haase ◽  
Aidong Chen ◽  
Francois Vandenberghe
Keyword(s):  
Near Infrared ◽  
Positive Impact ◽  
Storm Track ◽  
Precipitable Water ◽  
The United States ◽  
Severe Weather ◽  
Moist Atmosphere ◽  
Modis Data ◽  
Total Precipitable Water ◽  
The Impact

Abstract This study determined the accuracy and biases associated with retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water (TPW) data, and it investigated the impact of these data on severe weather simulations using the Weather Research and Forecast (WRF) model. Comparisons of MODIS TPW with the global positioning system (GPS) TPW and radiosonde-derived TPW were carried out. The comparison with GPS TPW over the United States showed that the root-mean-square (RMS) differences between these two datasets were about 5.2 and 3.3 mm for infrared (IR) and near-infrared (nIR) TPW, respectively. MODIS IR TPW data were overestimated in a dry atmosphere but underestimated in a moist atmosphere, whereas the nIR values were slightly underestimated in a dry atmosphere but overestimated in a moist atmosphere. Two cases, a severe thunderstorm system (2004) over land and Hurricane Isidore (2002) over ocean, as well as conventional observations and Special Sensor Microwave Imager (SSM/I) retrievals were used to assess the impact of MODIS nIR TPW data on severe weather simulations. The assimilation of MODIS data has a slightly positive impact on the simulated rainfall over Oklahoma for the thunderstorm case, and it was able to enhance Isidore’s intensity when the storm track was reasonably simulated. The use of original and bias-corrected MODIS nIR TPW did not show significant differences from both case studies. In addition, SSM/I data were found to have a positive impact on both severe weather simulations, and the impact was comparable to or slightly better than that of MODIS data.

scholarly journals Estimation of Local-scale Precipitable Water Vapor Distribution Around Each GNSS Station Using Slant Path Delay

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 29-33 ◽  
Author(s):  
Yoshinori Shoji ◽  
Hiroshi Yamauchi ◽  
Wataru Mashiko ◽  
Eiichi Sato
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Local Scale ◽  
Precipitable Water Vapor ◽  
Path Delay ◽  
Slant Path ◽  
Vapor Distribution
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