scholarly journals Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 182
Author(s):  
Anwei Lai ◽  
Jinzhong Min ◽  
Jidong Gao ◽  
Hedi Ma ◽  
Chunguang Cui ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Potential Temperature ◽  
Radar Data ◽  
Severe Weather ◽  
Moist Convection ◽  
Meiyu Front ◽  
Weather Events ◽  
Convective Scale ◽  
Deep Moist Convection

An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo- water vapor and pseudo- in- cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo- water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo- observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo- observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo- water vapor and potential temperature information was assimilated.

scholarly journals Assimilation of Radar Radial Velocity, Reflectivity, and Pseudo–Water Vapor for Convective-Scale NWP in a Variational Framework

2019 ◽  
Vol 147 (8) ◽  
pp. 2877-2900 ◽  
Author(s):  
Anwei Lai ◽  
Jidong Gao ◽  
Steven E. Koch ◽  
Yunheng Wang ◽  
Sijie Pan ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radial Velocity ◽  
Three Dimensional ◽  
Radar Data ◽  
Moist Convection ◽  
Severe Thunderstorm ◽  
Variational Framework ◽  
Convective Scale ◽  
Deep Moist Convection ◽  
Nwp Model

Abstract To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis.

Improving Operational Numerical Prediction of Afternoon Thunderstorms over Taiwan through Surface Data Assimilation

2020 ◽  
Author(s):  
I-Han Chen ◽  
Jing-Shan Hong ◽  
Ya-Ting Tsai ◽  
Chin-Tzu Fong
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Cold Pool ◽  
Moist Convection ◽  
Convective Systems ◽  
Forecast Lead Time ◽  
Deep Moist Convection ◽  
Outflow Boundary ◽  
Surface Observations

<p>Taiwan, a subtropical island with steep mountains, is influenced by diverse weather systems, including typhoons, monsoons, frontal, and convective systems. Of these, the prediction of deep, moist convection here is particularly challenging due to complex topography and apparent landsea contrast. This study explored the benefits of assimilating surface observations on prediction of afternoon thunderstorms using a 2-km resolution WRF and WRFDA model system with rapid update cycles. Consecutive afternoon thunderstorm events during 30 June to 08 July 2017 are selected. Five experiments, consisting of 240 continuous cycles are designed to evaluate the data assimilation strategy and observation impact. Statistical results show that assimilating surface observations systematically improves the accuracy of wind and temperature prediction near the surface. Also, assimilating surface observations alone in one-hour intervals improves model quantitative precipitation forecast (QPF) skill, extending the forecast lead time in the morning. Furthermore, radar data assimilation can benefit by the additional assimilation of surface observations, particularly for improving the model QPF skill for large rainfall thresholds. An afternoon thunderstorm event that occurred on 06 July 2017 is further examined. By assimilating surface and radar observations, the model is able to capture the timing and location of the convection. Consequently, the accuracy of the predicted cold pool and outflow boundary is improved, when compared to the surface observations.</p>

scholarly journals Planetary boundary layer evolution over the Amazon rain forest in episodes of deep moist convection at ATTO

2019 ◽  
Author(s):  
Maurício I. Oliveira ◽  
Otávio C. Acevedo ◽  
Matthias Sörgel ◽  
Ernani L. Nascimento ◽  
Antonio O. Manzi ◽  
...  
Keyword(s):  
Heat Flux ◽  
Potential Temperature ◽  
Sensible Heat Flux ◽  
Moist Convection ◽  
Early Afternoon ◽  
Virtual Potential Temperature ◽  
Gust Fronts ◽  
Deep Moist Convection ◽  
Tall Tower ◽  
Gust Front

Abstract. In this study, high-frequency, multi-level measurements performed from late October to mid-November of 2015 at a 80-m tall tower of the Amazon Tall Tower Observatory (ATTO) project in central Amazonas State, Brazil, were used to diagnose the evolution of thermodynamic and kinematic variables as well as scalar fluxes during the passage of outflows generated by deep moist convection (DMC). Outflow associated with DMC activity over or near the tall tower was identified through the analysis of storm echoes in base reflectivity data from S-band weather radar at Manaus, combined with the detection of gust fronts and cold pools utilizing tower data. Four outflow events were selected, three of which took place during the early evening transition or nighttime hours and one during the early afternoon. Results show that the magnitude of the drop in virtual potential temperature and changes in wind velocity during outflow passages vary according to the type, organization, and life cycle of the convective storm. Overall, the nocturnal events highlighted the passage of well-defined gust fronts with moderate decrease in virtual potential temperature and increase in wind speed. The early afternoon event lacked a sharp gust front and only a gradual drop in virtual potential temperature was observed, probably because of weak or undeveloped outflow. Sensible heat flux (H) experienced an increase at the time of gust front arrival, which was possibly due to sinking of colder air. This was followed by a prolonged period of negative H, associated with enhanced nocturnal negative H in the storms' wake. In turn, increased latent heat flux (LE) was observed following the gust front, owing to drier air coming from the outflow; however, malfunctioning of the moisture sensors during rain precluded a better assessment of this variable. Substantial enhancements of Turbulent Kinetic Energy (TKE) were observed during and after gust front passage, with values comparable to those measured in grass fire experiments, evidencing the highly turbulent character of convective outflows. The early afternoon event displayed slight decreases in the aforementioned quantities in the passage of the outflow. Finally, a conceptual model of the time evolution of H in nocturnal convective outflows observed at the tower site is presented.

scholarly journals A Spatial Filter Approach to Evaluating the Role of Convection on the Evolution of a Mesoscale Vortex

2013 ◽  
Vol 70 (7) ◽  
pp. 1954-1976 ◽  
Author(s):  
Glenn A. Creighton ◽  
Robert E. Hart ◽  
Philip Cunningham
Keyword(s):  
Three Dimensional ◽  
Spatial Separation ◽  
Spatial Filter ◽  
Tropical Cyclogenesis ◽  
Moist Convection ◽  
Mesoscale Vortex ◽  
Convective Scale ◽  
Vortical Hot Towers ◽  
Order Of Magnitude ◽  
Deep Moist Convection

Abstract A new spatial filter is proposed that exploits a spectral gap in power between the convective scale and the system (“vortex”) scale during tropical cyclone (TC) genesis simulations. Using this spatial separation, this study analyzes idealized three-dimensional numerical simulations of deep moist convection in the presence of a symmetric midlevel vortex to quantify and understand the energy cascade between the objectively defined convective scale and system scale during the early stages of tropical cyclogenesis. The simulations neglect surface momentum, heat, and moisture fluxes to focus on generation and enhancement of vorticity within the interior to more completely close off the energy budget and to be consistent for comparison with prior benchmark studies of modeled TC genesis. The primary contribution to system-scale intensification comes from the convergence of convective-scale vorticity that is supplied by vortical hot towers (VHTs). They contribute more than the convergence of system-scale vorticity to the spinup of vorticity in these simulations by an order of magnitude. Analysis of the change of circulation with time shows an initial strengthening of the surface vortex, closely followed by a growth of the mid- to upper-level circulation. This evolution precludes any possibility of a stratiform precipitation–induced top-down mechanism as the primary contributor to system-scale spinup in this simulation. Instead, an upscale cascade of rotational kinetic energy during vortex mergers is responsible for spinup of the simulated mesoscale vortex. The spatial filter employed herein offers an alternative approach to the traditional symmetry–asymmetry paradigm, acknowledges the highly asymmetric evolution of the system-scale vortex itself, and may prove useful to future studies on TC genesis.

scholarly journals Planetary boundary layer evolution over the Amazon rainforest in episodes of deep moist convection at the Amazon Tall Tower Observatory

2020 ◽  
Vol 20 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Maurício I. Oliveira ◽  
Otávio C. Acevedo ◽  
Matthias Sörgel ◽  
Ernani L. Nascimento ◽  
Antonio O. Manzi ◽  
...  
Keyword(s):  
Heat Flux ◽  
Potential Temperature ◽  
Sensible Heat Flux ◽  
Moist Convection ◽  
Early Afternoon ◽  
Virtual Potential Temperature ◽  
Gust Fronts ◽  
Deep Moist Convection ◽  
Tall Tower ◽  
Gust Front

Abstract. In this study, high-frequency, multilevel measurements, performed from late October to mid-November of 2015 at a 80 m tall tower of the Amazon Tall Tower Observatory (ATTO) project in the central state of Amazonas, Brazil, were used to diagnose the evolution of thermodynamic and kinematic variables as well as scalar fluxes during the passage of outflows generated by deep moist convection (DMC). Outflow associated with DMC activity over or near the tall tower was identified through the analysis of storm echoes in base reflectivity data from an S-band weather radar at Manaus, combined with the detection of gust fronts and cold pools utilizing tower data. Four outflow events were selected, three of which took place during the early evening transition or nighttime hours and one during the early afternoon. Results show that the magnitude of the drop in virtual potential temperature and changes in wind velocity during outflow passages vary according to the type, organization, and life cycle of the convective storm. The nocturnal events had well-defined gust fronts with moderate decreases in virtual potential temperature and increases in wind speed. The early afternoon event lacked a sharp gust front and only a gradual drop in virtual potential temperature was observed, probably because of weak or undeveloped outflow. Sensible heat flux (H) increased at the time of the gust front arrival, which was possibly due to the sinking of colder air. This was followed by a prolonged period of negative H, associated with enhanced nocturnal negative H in the wake of the storms. In turn, increased latent heat flux (LE) was observed following the gust front, owing to drier air coming from the outflow; however, malfunctioning of the moisture sensors during rain precluded a better assessment of this variable. Substantial enhancements of turbulent kinetic energy (TKE) were observed during and after the gust front passage, with values comparable to those measured in grass fire experiments, evidencing the highly turbulent character of convective outflows. The early afternoon event displayed slight decreases in the aforementioned quantities in the passage of the outflow. Finally, a conceptual model of the time evolution of H in nocturnal convective outflows observed at the tower site is presented.

scholarly journals Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

2014 ◽  
Vol 142 (11) ◽  
pp. 4017-4035 ◽  
Author(s):  
Yu-Chieng Liou ◽  
Jian-Luen Chiou ◽  
Wei-Hao Chen ◽  
Hsin-Yu Yu
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Scale Up ◽  
Three Dimensional ◽  
Radar Data ◽  
State Variables ◽  
Weather System ◽  
Additional Information ◽  
Convective Scale ◽  
Fine Resolution

Abstract This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model. Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as four-dimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE): Project overview

2020 ◽  
Author(s):  
Karina Wilgan ◽  
Jens Wickert ◽  
Galina Dick ◽  
Florian Zus ◽  
Torsten Schmidt ◽  
...  
Keyword(s):  
Water Vapor ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Spatiotemporal Resolution ◽  
Weather Forecasts ◽  
Precise Knowledge ◽  
Weather Events ◽  
Data Products ◽  
New Generation ◽  
The Impact

<p>Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently established as a powerful and versatile observation tool for geosciences. An outstanding application in this context is the operational monitoring of atmospheric water vapor with high spatiotemporal resolution. The water vapor is the most abundant greenhouse gas, which accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is a precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts especially with focus to the strong precipitation and severe weather events.</p><p>The data from European GNSS networks are widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the exclusively focusing on GPS-only based data products; to the limited atmospheric information content, which is provided mostly in the zenith direction and to the time delay between measurement and providing the data products, which is currently about one hour.</p><p>AMUSE is a recent research project, funded by the DFG (German Research Council) and performed in close cooperation of TUB, GFZ and DWD during 2020-2022. The project foci are the major limitations of currently operationally used generation of GNSS-based water vapor data. AMUSE will pioneer the development of next generation data products. Main addressed innovations are:  1) Developments to provide multi-GNSS instead of GPS-only data, including GLONASS, Galileo and BeiDou; 2) Developments to provide high quality slant observations, containing water vapor information along the line-of-sight from the respective ground stations; 3) Developments to shorten the delay between measurements and the provision of the products to the meteorological services.</p><p>This GNSS-focused work of AMUSE will be complemented by the contribution of German Weather Service DWD to investigate in detail and to quantify the forecast improvement, which can be reached by the new generation GNSS-based meteorology data. Several dedicated forecast experiments will be conducted with focus on one of the most challenging issues, the precipitation forecast in case of severe weather events. These studies will support the future assimilation of the new generation data to the regional forecast system of DWD and potentially also to other European weather services.</p>

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>

scholarly journals Application of a WRF Mesoscale Data Assimilation System to Springtime Severe Weather Events 2007–09

2012 ◽  
Vol 140 (5) ◽  
pp. 1539-1557 ◽  
Author(s):  
Dustan M. Wheatley ◽  
David J. Stensrud ◽  
David C. Dowell ◽  
Nusrat Yussouf
Keyword(s):  
Data Assimilation ◽  
Ensemble Forecast ◽  
Severe Weather ◽  
Convective System ◽  
Weather Events ◽  
Short Period ◽  
Grid Points ◽  
Mean Square Errors ◽  
Data Assimilation System ◽  
Assimilation System

Abstract An ensemble-based data assimilation system using the Weather Research and Forecasting Model (WRF) has been used to initialize forecasts of prolific severe weather events from springs 2007 to 2009. These experiments build on previous work that has shown the ability of ensemble Kalman filter (EnKF) data assimilation to produce realistic mesoscale features, such as drylines and convectively driven cold pools, which often play an important role in future convective development. For each event in this study, severe weather parameters are calculated from an experimental ensemble forecast started from EnKF analyses, and then compared to a control ensemble forecast in which no ensemble-based data assimilation is performed. Root-mean-square errors for surface observations averaged across all events are generally smaller for the experimental ensemble over the 0–6-h forecast period. At model grid points nearest to tornado reports, the ensemble-mean significant tornado parameter (STP) and the probability that STP > 1 are often greater in the experimental 0–6-h ensemble forecasts than in the control forecasts. Likewise, the probability of mesoscale convective system (MCS) maintenance probability (MMP) is often greater with the experimental ensemble at model grid points nearest to wind reports. Severe weather forecasts can be sharpened by coupling the respective severe weather parameter with the probability of measurable rainfall at model grid points. The differences between the two ensembles are found to be significant at the 95% level, suggesting that even a short period of ensemble data assimilation can yield improved forecast guidance for severe weather events.

scholarly journals Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments

2015 ◽  
Vol 30 (6) ◽  
pp. 1795-1817 ◽  
Author(s):  
Dustan M. Wheatley ◽  
Kent H. Knopfmeier ◽  
Thomas A. Jones ◽  
Gerald J. Creager
Keyword(s):  
Data Assimilation ◽  
Leading Edge ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
Low Level ◽  
The North ◽  
Weather Events ◽  
Probabilistic Forecasts ◽  
Bow Echo ◽  
System Part

Abstract This first part of a two-part study on storm-scale radar and satellite data assimilation provides an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) project. The NSSL Experimental WoF System for ensembles (NEWS-e) is used to produce storm-scale analyses and forecasts of six diverse severe weather events from spring 2013 and 2014. In this study, only Doppler reflectivity and radial velocity observations (and, when available, surface mesonet data) are assimilated into a 36-member, storm-scale ensemble using an ensemble Kalman filter (EnKF) approach. A series of 1-h ensemble forecasts are then initialized from storm-scale analyses during the 1-h period preceding the onset of storm reports. Of particular interest is the ability of these 0–1-h ensemble forecasts to reproduce the low-level rotational characteristics of supercell thunderstorms, as well as other convective hazards. For the tornado-producing thunderstorms considered in this study, ensemble probabilistic forecasts of low-level rotation generally indicated a rotating thunderstorm approximately 30 min before the time of first observed tornado. Displacement errors (often to the north of tornado-affected areas) associated with vorticity swaths were greatest in those forecasts launched 30–60 min before the time of first tornado. Similar forecasts were produced for a tornadic mesovortex along the leading edge of a bow echo and, again, highlighted a well-defined vorticity swath as much as 30 min prior to the first tornado.

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