Forecast Sensitivity of the Aircraft Observations Impact in the WRF 3DVAR Data Assimilation Systems on Guarulhos International Airport Forecast

2021 ◽  
Author(s):  
Ricardo Da Silva ◽  
Gutemberg França
Keyword(s):  
Data Assimilation ◽  
Reporting System ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Precipitation Forecast ◽  
Cloud Base ◽  
International Airport ◽  
Aircraft Observations ◽  
Aircraft Data

<p>Despite of it is well known, it is always good to point that numerical weather prediction is an initial value problem and requires analysis of the initial conditions to begin a time dependent process (Richardson, 1922). Bergthorsson and Döös (1955), in that time, enunciated that analysis could be improved if they were not based solely on available observations, but also on forecasts made by model from previous observations, with background on data assimilation defined usually by a model forecast with errors. Airports are the most weather info powered locations, although all infrastructure, most of the moisture, turbulence, and convective processes circle around 25,000 feet and below, what turns rawinsonde observations an important source, besides, off course, data observations obtained from aircrafts. The aircraft data universe includes the Aircraft Communications Addressing and Reporting System (ACARS) reporting temperature and wind collected during all phases of flight, which composes the subset named Meteorological Data Collection and Reporting System (MDCRS), which has been used by several air carriers. For example, the AMDAR (Aircraft Meteorological Data Relay) program delivers more than 680,000 wind and temperature reports daily (Petersen et al., 2015), and with the advent of humidity sensor (Water Vapor Sensing System - WVSS-II in Hoover et al., 2017), vertical profiles of moisture (ascent and descent) are included in that. Based on current ECMWF numbers, FM-35 WMO provides 413 thousand information’s in the assimilation cycle for a typical day, otherwise, aircraft observations provide 1,234 thousands information’s (Bonavita in ECMWF, 2020). Numbers obtained from MADIS support page (amdar.noaa.gov/new_soundings) shows that on Guarulhos Airport receiving 835 (eight hundred and third five) profiles on period from July, 14 to 20, 2019. It takes a more important role, when it comes to mind that satellites profiles cannot resolve sharp vertical structures, as an example, warming-moisture combination to thunderstorm development. For testing the forecast sensitivity of the aircraft observations impact in the WRF 3DVAR Data Assimilation Systems, the WRF 4.2.1 has been installed without any source code modification, and configured for a 36 hour simulation period in forecast mode, starting in 12Z January, 2<sup>nd</sup> 2020, applying Global Forecast System (GFS) model as initial and boundary condition, for a centred area in Guarulhos Airport, with 9 km spatial resolution. The results were compared against a simulation including aircraft data observation obtained from MADIS for Guarulhos International Airport Forecast, for the same period. That date was marked with strong precipitation starting around 19Z, with damages to the Airport infrastructure, as well, causing flight operations impact. For this period two profiles have been obtained and applied in the window time around analysis (12Z January, 2<sup>nd</sup> 2020), and both assimilated using 3DVar WRF System. Analysis based on the results obtained demonstrates that there was an increase in precipitation amount forecasted by assimilation experiment and cooling temperature in cloud base, against no-assimilation, leading to conclusion that the aircraft profile data assimilation process can impact a precipitation forecast even 7 hours after analysis, encouraging to apply a 4DVar, in short range forecast and more assimilation experiments.</p>

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 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 Regional Aircraft Observations Using TAMDAR

2010 ◽  
Vol 25 (2) ◽  
pp. 627-645 ◽  
Author(s):  
William R. Moninger ◽  
Stanley G. Benjamin ◽  
Brian D. Jamison ◽  
Thomas W. Schlatter ◽  
Tracy Lorraine Smith ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Meteorological Data ◽  
Time Range ◽  
Observation System ◽  
Vertical Resolution ◽  
Commercial Aircraft ◽  
Aircraft Observation ◽  
Aircraft Observations ◽  
High Vertical Resolution

Abstract A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts—particularly for relative humidity forecasts—that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR’s impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDAR-equipped aircraft operated during the studied period of analysis.

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

scholarly journals Assimilation of GPM-retrieved Ocean Surface Meteorology Data for Two Snowstorm Events during ICE-POP 2018

2021 ◽  
Author(s):  
Xuanli Li ◽  
Jason B. Roberts ◽  
Jayanthi Srikishen ◽  
Jonathan L. Case ◽  
Walter A. Petersen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Wrf Model ◽  
Field Research ◽  
Ocean Surface ◽  
Positive Influence ◽  
Precipitation Forecast ◽  
Specific Humidity ◽  
Wind Fields

Abstract. As a component of the National Aeronautics and Space Administration (NASA) Weather Focus Area and GPM Ground Validation participation in the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the Global Precipitation Measurement (GPM) microwave observations for January – March 2018. In this study, the retrieved ocean surface meteorological products – 2-m temperature, 2-m specific humidity, and 10-m wind speed were assimilated into a regional numerical weather prediction (NWP) framework to explore the application of these observations for two heavy snowfall events during the ICE-POP 2018: 27–28 February, and 7–8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, positive influence of the retrieved surface observation was found for the March case with improved quantitative precipitation forecast and reduced error in temperature forecast. A slightly smaller yet positive impact was also found in the forecast of the February case.

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 Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics

2016 ◽  
Vol 31 (1) ◽  
pp. 217-236 ◽  
Author(s):  
María E. Dillon ◽  
Yanina García Skabar ◽  
Juan Ruiz ◽  
Eugenia Kalnay ◽  
Estela A. Collini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Wrf Model ◽  
Convective System ◽  
Efficient Manner ◽  
Maryland College ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.

scholarly journals Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015)

2016 ◽  
Vol 30 (2) ◽  
pp. 112
Author(s):  
Novvria Sagita ◽  
Rini Hidayati ◽  
Rahmat Hidayat ◽  
Indra Gustari ◽  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Dew Point ◽  
Radiosonde Data ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Condition Model ◽  
Weather Research

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy  of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o  C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation.

scholarly journals Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Wrf Model ◽  
Analysis Data ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Precipitation Analysis ◽  
The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

scholarly journals Data assimilation of radar reflectivity volumes in a LETKF scheme

2018 ◽  
Vol 25 (4) ◽  
pp. 747-764 ◽  
Author(s):  
Thomas Gastaldo ◽  
Virginia Poli ◽  
Chiara Marsigli ◽  
Pier Paolo Alberoni ◽  
Tiziana Paccagnella
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Observational Error ◽  
The Impact

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

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