Predicting Typhoon Morakot’s Catastrophic Rainfall with a Convection-Permitting Mesoscale Ensemble System

2010 ◽  
Vol 25 (6) ◽  
pp. 1816-1825 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng ◽  
Ying-Hwa Kuo ◽  
Jeffery S. Whitaker ◽  
Baoguo Xie
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Decision Makers ◽  
Extreme Weather Events ◽  
Management Decision ◽  
Typhoon Morakot ◽  
Weather Events ◽  
Flooding Event

Abstract This study examines the prediction and predictability of the recent catastrophic rainfall and flooding event over Taiwan induced by Typhoon Morakot (2009) with a state-of-the-art numerical weather prediction model. A high-resolution convection-permitting mesoscale ensemble, initialized with analysis and flow-dependent perturbations obtained from a real-time global ensemble data assimilation system, is found to be able to predict this record-breaking rainfall event, producing probability forecasts potentially valuable to the emergency management decision makers and the general public. Since all the advanced modeling and data assimilation techniques used here are readily available for real-time operational implementation provided sufficient computing resources are made available, this study demonstrates the potential and need of using ensemble-based analysis and forecasting, along with enhanced computing, in predicting extreme weather events like Typhoon Morakot at operational centers.

scholarly journals Data assimilation for an operational nowcasting tool

2021 ◽  
Author(s):  
Ivan R. Gelpi ◽  
Aurelio Diaz de Arcaya ◽  
Xabier Pedruzo ◽  
Santiago Gaztelumendi
Keyword(s):  
Data Assimilation ◽  
Basque Country ◽  
Weather Prediction ◽  
Extreme Weather Events ◽  
Limited Area ◽  
Special Focus ◽  
Numerical Representation ◽  
Weather Events ◽  
Weather Stations ◽  
Assimilation System

<p>In the Basque Meteorology Agency (EUSKALMET), numerical weather prediction (NWP) models, adapted to the particular characteristics of the territory, are executed daily for many different purposes. In order to improve nowcasting and forecasting tasks, a WRFDA base data assimilation tool, was implemented. Assimilation of meteorological data combines the information provided by measured data with the information coming from numerical models, supplying the numerical representation more consistent with observations.</p><p>Working with continuous assimilation-forecast cycles of the assimilation system allows constant updating of limited area forecasts, improving nowcasting tasks, especially severe weather events. Nowadays, the tool is being executed routinely in operational basis. The assimilation system includes several datasets from different sources (surface and upper air data), available in the forecast domains: RAOB soundings, SYNOP, Buoy, METAR, Automatic weather stations and Radar. The Basque Country Weather Mesonet, managed by EUSKALMET, is a high-density network with more than 100 Automatic Weather Stations (AWS), representative of a territory of complex orography such as the Basque Country. Some observations registered in this network (ten-minute data) are included on the Data assimilation system. Euskalmet Radar is a METEOR 1500 Doppler Weather Radar with Dual polarization capabilities located on Kapildui mountain top (1174 m). Two volumetric scan are available each 10 minutes (range 300 km in reflectivity mode, range 150km in Doppler/Reflectivity mode). Reflectivity data is included in assimilation cycles.</p><p>The objective of this paper is to present the assimilation system included in the tool and to explain the results of some sensitivity experiments during high-impact weather events, to test the system's skill nowcasting extreme weather events. We present different validation analysis based on punctual and areal approaches. With a special focus on the use of datasets from the Basque Country Automatic Weather Station Mesonetwork and the available radar data.</p>

scholarly journals RELATIONSHIP BETWEEN HIMAWARI-8-DERIVED OVERSHOOTING TOPS AND EXTREME WEATHER EVENTS IN SOUTHEAST ASIA

2016 ◽  
Vol XLI-B7 ◽  
pp. 325-327
Author(s):  
H. M. Park ◽  
M. A. Kim ◽  
J. Im
Keyword(s):  
Heavy Rainfall ◽  
Rain Rate ◽  
Deep Convection ◽  
Weather Prediction ◽  
Extreme Weather Events ◽  
Strong Wind ◽  
Atmospheric Conditions ◽  
Machine Learning Methods ◽  
Weather Events ◽  
Strong Convection

Severe weathers such as heavy rainfall, floods, strong wind, and lightning are closely related with the strong convection activities of atmosphere. Overshooting tops sometimes occur by deep convection above tropopause, penetrating into the lower stratosphere. Due to its high potential energy, the detection of OT is crucial to understand the climatic phenomena. Satellite images are useful to detect the dynamics of atmospheric conditions using cloud observation. This study used machine learning methods for extracting OTs. The reference cases were built using CloudSat, CALIPSO, and Numerical Weather Prediction (NWP) data with Himawari-8 imagery. As reference cases, 11 OT events were detected. The aim of this study is the investigation of relationship between OTs cases and the occurrences of heavy rainfall. For investigation of OT effects, TRMM daily rain rate data (mm/hr) were collected and averaged at 25 km intervals until 250km from the center of OT cases. As the result, precipitation rate clearly coincides with the distance from the center of OT occurrence.

scholarly journals Driftsonde Observations to Evaluate Numerical Weather Prediction of the Late 2006 African Monsoon

2013 ◽  
Vol 52 (4) ◽  
pp. 974-995 ◽  
Author(s):  
Philippe Drobinski ◽  
Fatima Karbou ◽  
Peter Bauer ◽  
Philippe Cocquerez ◽  
Christophe Lavaysse ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Vertical Structure ◽  
Weather Prediction ◽  
Meridional Wind ◽  
Numerical Weather Prediction Model ◽  
African Monsoon ◽  
Easterly Waves ◽  
Numerical Weather ◽  
Multidisciplinary Analysis

AbstractDuring the international African Monsoon Multidisciplinary Analysis (AMMA) project, stratospheric balloons carrying gondolas called driftsondes capable of dropping meteorological sondes were deployed over West Africa and the tropical Atlantic Ocean. The goals of the deployment were to test the technology and to study the African easterly waves, which are often the forerunners of hurricanes. Between 29 August and 22 September 2006, 124 sondes were dropped over the seven easterly waves that moved across Africa into the Atlantic between about 10° and 20°N, where almost no in situ vertical information exists. Conditions included waves that developed into Tropical Storm Florence and Hurricanes Gordon and Helene. In this study, a selection of numerical weather prediction model outputs has been compared with the dropsondes to assess the effect of some developments in data assimilation on the quality of analyses and forecasts. By comparing two different versions of the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model of Météo-France with the dropsondes, first the benefits of the last data assimilation updates are quantified. Then comparisons are carried out using the ARPEGE model and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts. It is shown that the two models represent very well the vertical structure of temperature and humidity over both land and sea, and particularly within the Saharan air layer, which displays humidity below 5%–10%. Conversely, the models are less able to represent the vertical structure of the meridional wind. This problem seems to be common to ARPEGE and IFS, and its understanding still requires further investigations.

scholarly journals Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

2018 ◽  
Vol 33 (2) ◽  
pp. 599-607 ◽  
Author(s):  
John R. Lawson ◽  
John S. Kain ◽  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Dustan M. Wheatley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Radar Data ◽  
Model Systems ◽  
Deterministic Systems ◽  
Forecast Lead Time ◽  
The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

scholarly journals Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011

2012 ◽  
Vol 140 (8) ◽  
pp. 2609-2627 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Edward R. Mansell ◽  
Conrad L. Ziegler ◽  
Donald R. MacGorman
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Numerical Weather Prediction Model ◽  
Analysis Time ◽  
Test Case ◽  
Forecast Errors ◽  
Mixing Ratio ◽  
Total Lightning ◽  
Tornado Outbreak

Abstract This study presents the assimilation of total lightning data to help initiate convection at cloud-resolving scales within a numerical weather prediction model. The test case is the 24 May 2011 Oklahoma tornado outbreak, which was characterized by an exceptional synoptic/mesoscale setup for the development of long-lived supercells with large destructive tornadoes. In an attempt to reproduce the observed storms at a predetermined analysis time, total lightning data were assimilated into the Weather Research and Forecasting Model (WRF) and analyzed via a suite of simple numerical experiments. Lightning data assimilation forced deep, moist precipitating convection to occur in the model at roughly the locations and intensities of the observed storms as depicted by observations from the National Severe Storms Laboratory’s three-dimensional National Mosaic and Multisensor Quantitative Precipitation Estimation (QPE)—i.e., NMQ—radar reflectivity mosaic product. The nudging function for the total lightning data locally increases the water vapor mixing ratio (and hence relative humidity) via a simple smooth continuous function using gridded pseudo-Geostationary Lightning Mapper (GLM) resolution (9 km) flash rate and simulated graupel mixing ratio as input variables. The assimilation of the total lightning data for only a few hours prior to the analysis time significantly improved the representation of the convection at analysis time and at the 1-h forecast within the convective permitting and convective resolving grids (i.e., 3 and 1 km, respectively). The results also highlighted possible forecast errors resulting from errors in the initial mesoscale thermodynamic variable fields. Although this case was primarily an analysis rather than a forecast, this simple and computationally inexpensive assimilation technique showed promising results and could be useful when applied to events characterized by moderate to intense lightning activity.

scholarly journals Path Properties of Atmospheric Transitions: Illustration with a Low-Order Sudden Stratospheric Warming Model

2020 ◽  
Vol 77 (7) ◽  
pp. 2327-2347
Author(s):  
Justin Finkel ◽  
Dorian S. Abbot ◽  
Jonathan Weare
Keyword(s):  
Human Life ◽  
Weather Prediction ◽  
Extreme Weather Events ◽  
Future Application ◽  
Type Model ◽  
Stratospheric Warming ◽  
Sudden Stratospheric Warming ◽  
Weather Events ◽  
Probabilistic Forecasts ◽  
Low Order

AbstractMany rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.

scholarly journals Real time implementation of agricultural contingency plans to cope with weather aberrations in Indian agriculture

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 183-194
Author(s):  
CH. SRINIVASA RAO RAO ◽  
G. RAVINDRA CHARY ◽  
N. RANI ◽  
V. S. BAVISKAR
Keyword(s):  
Real Time ◽  
Extreme Events ◽  
Agricultural Production ◽  
Extreme Weather Events ◽  
Horticultural Crops ◽  
Indian Agriculture ◽  
Weather Events ◽  
Overall Stability ◽  
Droughts And Floods ◽  
Seasonal Droughts

Weather aberrations impact agriculture and allied sectors in one or other parts of the India round the year. Seasonal droughts and extreme weather events in 21st century have caused alarming losses not only in agricultural production but also horticulture, livestock, poultry and fisheries. ICAR-CRIDA, SAUs and DAC, MoA, GoI, prepared more than 580 district level agriculture plans within formation on contingency measures for sustaining higher agriculture production and to cope with extreme events. Real-time contingency planning (RTCP) is being conceptualized and implemented at micro level in farmers’ fields in this country. RTCP implementation during delayed onset of monsoon, seasonal droughts and floods resulted in better crop performance, higher agricultural production, better incomes and overall stability in house-hold livelihoods. In this paper, the real-contingency measures to cope with extreme events for management of horticultural crops, livestock, poultry and fisheries are proposed. Further, the preparedness for RTCP implementation with policy initiatives is also suggested.

scholarly journals Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018 ◽  
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Precipitation Prediction ◽  
Cell Patterns ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Pattern Presentation ◽  
Radar Data Assimilation

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

Improved Prediction of Landfalling Tropical Cyclone in China Based on Assimilation of Radar Radial Winds with New Super-Observation Processing

2020 ◽  
Vol 35 (6) ◽  
pp. 2523-2539
Author(s):  
Jianing Feng ◽  
Yihong Duan ◽  
Qilin Wan ◽  
Hao Hu ◽  
Zhaoxia Pu
Keyword(s):  
Data Assimilation ◽  
Inner Core ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Previous Method ◽  
Horizontal Wind ◽  
The Impact

AbstractThis work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

scholarly journals On the extraction of wind information from the assimilation of ozone profiles in Météo–France 4-D-Var operational NWP suite

2009 ◽  
Vol 9 (14) ◽  
pp. 4855-4867 ◽  
Author(s):  
N. Semane ◽  
V.-H. Peuch ◽  
S. Pradier ◽  
G. Desroziers ◽  
L. El Amraoui ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Degrees Of Freedom ◽  
Variance Reduction ◽  
Lower Stratosphere ◽  
Transport Model ◽  
Weather Prediction ◽  
Error Variance ◽  
Numerical Weather Prediction Model ◽  
Background Error ◽  
Better Than

Abstract. By applying four-dimensional variational data-assimilation (4-D-Var) to a combined ozone and dynamics Numerical Weather Prediction model (NWP), ozone observations generate wind increments through the ozone-dynamics coupling. The dynamical impact of Aura/MLS satellite ozone profiles is investigated using Météo-France operational ARPEGE NWP 4-D-Var assimilation system for a period of 3 months. A data-assimilation procedure has been designed and run on 6-h windows. The procedure includes: (1) 4-D-Var assimilating both ozone and operational NWP standard observations, (2) ARPEGE transporting ozone as a passive-tracer, (3) MOCAGE, the Météo–France chemistry and transport model re-initializing the ARPEGE ozone background at the beginning time of the assimilation window. Using observation minus forecast statistics, it is found that the ozone assimilation reduces the wind bias in the lower stratosphere. Moreover, the Degrees of Freedom for Signal diagnostics show that the MLS data covering the 68.1–31.6 hPa vertical pressure range are the most informative and their information content is nearly of the same order as tropospheric humidity-sensitive radiances. Furthermore, with the help of error variance reduction diagnostics, the ozone contribution to the reduction of the horizontal divergence background-error variance is shown to be better than tropospheric humidity-sensitive radiances.

Sign in / Sign up

Export Citation Format

Share Document