Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.

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Synoptic and Mesoscale Background of the Disastrous Heavy Rainfall in Sri Lanka Caused by a Low Pressure System

10.1007/978-3-030-73003-1_27 ◽

2021 ◽

pp. 407-416

Author(s):

A. R. P. Warnasooriya ◽

K. H. M. S. Premalal ◽

A. W. S. J. Kumara ◽

Chathuska G. Premachandra

Keyword(s):

Sri Lanka ◽

Heavy Rainfall ◽

Low Pressure ◽

Pressure System ◽

Low Pressure System

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Evaluation of Two Cloud-Resolving Models Using Bin or Bulk Microphysics Representation for the HyMeX-IOP7a Heavy Precipitation Event

Atmosphere ◽

10.3390/atmos11111177 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1177

Author(s):

Diana Arteaga ◽

Céline Planche ◽

Christina Kagkara ◽

Wolfram Wobrock ◽

Sandra Banson ◽

...

Keyword(s):

Hydrological Cycle ◽

Wrf Model ◽

Heavy Precipitation ◽

Sensitivity Study ◽

Precipitation Event ◽

Convective System ◽

Wind Fields ◽

Massif Central ◽

Surface Precipitation ◽

Low Level

The Mediterranean region is frequently affected in autumn by heavy precipitation that causes flash-floods or landslides leading to important material damage and casualties. Within the framework of the international HyMeX program (HYdrological cycle in Mediterranean EXperiment), this study aims to evaluate the capabilities of two models, WRF (Weather Research and Forecasting) and DESCAM (DEtailed SCAvenging Model), which use two different representations of the microphysics to reproduce the observed atmospheric properties (thermodynamics, wind fields, radar reflectivities and precipitation features) of the HyMeX-IOP7a intense precipitating event (26 September 2012). The DESCAM model, which uses a bin resolved representation of the microphysics, shows results comparable to the observations for the precipitation field at the surface. On the contrary, the simulations made with the WRF model using a bulk representation of the microphysics (either the Thompson scheme or the Morrison scheme), commonly employed in NWP models, reproduce neither the intensity nor the distribution of the observed precipitation—the rain amount is overestimated and the most intense cell is shifted to the East. The different simulation results show that the divergence in the surface precipitation features seems to be due to different mechanisms involved in the onset of the precipitating system: the convective system is triggered by the topography of the Cévennes mountains (i.e., south-eastern part of the Massif Central) in DESCAM and by a low-level flux convergence in WRF. A sensitivity study indicates that the microphysics properties have impacted the thermodynamics and dynamics fields inducing the low-level wind convergence simulated with WRF for this HyMeX event.

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Blue whales off the Southern coast of Sri Lanka during the Southwest Monsoon Season

J. Cetacean Res. Manage. ◽

10.47536/jcrm.v21i1.189 ◽

2020 ◽

Vol 21 (1) ◽

pp. 1-7

Author(s):

Grace Russell ◽

Marcus Bridge ◽

Maja Nimak-Wood

Keyword(s):

Sri Lanka ◽

Monsoon Season ◽

Weather Conditions ◽

Southwest Monsoon ◽

Lower Latitude ◽

Northeast Monsoon ◽

Southern Coast ◽

Northern India ◽

Blue Whales ◽

Southwest Monsoon Season

Observations of 37 individual blue whales (Balaenoptera musculus) were recorded off the southern coast of Sri Lanka during the Southwest Monsoon Season (SWM). Sightings were made during a scientific geophysical survey campaign conducted in July and August 2017. Whilst blue whales are regularly recorded on the continental slope of southern Sri Lanka during the Northeast Monsoon Season (NEM) (December - March) and during the two inter-monsoonal periods (March - April and September - October), limited data is available for the SWM (May - September) mostly due to unfavourable weather conditions and very little survey effort. In the northern hemisphere blue whales undertake seasonal migrations from higher latitude feeding grounds to lower latitude breeding and wintering areas. However it has been suggested that a population of blue whales in the Northern India Ocean (NIO) remains in lower latitudes year round taking advantage of the rich upwelling areas off Somalia, southwest Arabia and western Sri Lanka. Data from this study nevertheless support a theory that a certain number of individuals remain off the southern coast off Sri Lanka during the SWM, suggesting that the productivity in this region is sufficient to support their year-round presence. This study therefore fills a knowledge gap regarding the presence and movement of blue whales in the NIO highlighting the importance of data obtained from platforms of opportunity.

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ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO

Ciência e Natura ◽

10.5902/2179460x20193 ◽

2016 ◽

Vol 38 ◽

pp. 491

Author(s):

Lissette Guzmán Rodríguez ◽

Vagner Anabor ◽

Franciano Scremin Puhales ◽

Everson Dal Piva

Keyword(s):

Wrf Model ◽

Random Variable ◽

Heavy Rain ◽

Heavy Precipitation ◽

Precipitation Forecast ◽

Precipitation Event ◽

Ensemble Prediction ◽

False Alarms ◽

Heavy Precipitation Event ◽

Skill Scores

In this paper was used the kernel density estimation (KDE), a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic precipitation forecast, from an ensemble prediction with the WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables, for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.

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Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System

Water ◽

10.3390/w12071918 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1918

Author(s):

Jiaying Zhang ◽

Liao-Fan Lin ◽

Rafael L. Bras

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Wrf Model ◽

Heavy Precipitation ◽

Convective Precipitation ◽

Regular Space ◽

Observation Error ◽

Central United States ◽

Precipitation Estimates ◽

Constant Observation

Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system using a four-dimensional variational (4D-Var) method. Three synoptic-scale convective precipitation events over the central United States during 2015–2017 are used as case studies. To investigate the effect of logarithmically transformed IMERG precipitation in the WRFDA system, this study reports on several experiments with six-hour and hourly assimilation windows, regular (nontransformed) and logarithmically transformed observations, and a constant observation error in regular and logarithmic spaces. Results show that hourly assimilation windows improve precipitation simulations significantly compared to six-hour windows. Logarithmically transformed precipitation does not improve precipitation estimations relative to nontransformed precipitation. However, better predictions of heavy precipitation can be achieved with a constant error in the logarithmic space (corresponding to a linearly increasing error in the regular space), which modifies the threshold of rejecting observations, and thus utilizes more observations. This study provides a cost function with logarithmically transformed observations for the 4D-Var method in the WRFDA system for future investigations.

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The Role of Typhoon Songda (2004) in Producing Distantly Located Heavy Rainfall in Japan*

Monthly Weather Review ◽

10.1175/2009mwr2933.1 ◽

2009 ◽

Vol 137 (11) ◽

pp. 3699-3716 ◽

Cited By ~ 49

Author(s):

Yongqing Wang ◽

Yuqing Wang ◽

Hironori Fudeyasu

Keyword(s):

Heavy Rainfall ◽

Control Experiment ◽

Wrf Model ◽

Heavy Precipitation ◽

Precipitation Event ◽

Initial Time ◽

Heavy Rainfall Event ◽

Arw Model ◽

Southern Central ◽

Environmental Prediction

Abstract When Typhoon Songda (2004) was located southeast of Okinawa over the western North Pacific during 2–4 September 2004, a heavy rainfall event occurred over southern central Japan and its adjacent seas, more than 1200 km from the typhoon center. The Advanced Research version of the Weather Research and Forecast (WRF-ARW) model was used to investigate the possible remote effects of Typhoon Songda on this heavy precipitation event in Japan. The National Centers for Environmental Prediction (NCEP) global final (FNL) analysis was used to provide both the initial and lateral boundary conditions for the WRF model. The model was initialized at 1800 UTC 2 September and integrated until 1800 UTC 6 September 2004, during which Songda was a supertyphoon. Two primary numerical experiments were performed. In the control experiment, a bogus vortex was inserted into the FNL analysis to enhance the initial storm intensity such that the model typhoon had an intensity that was similar to that observed at the initial time. In the no-typhoon experiment, the vortex associated with Typhoon Songda in the FNL analysis was removed by a smoothing algorithm such that the typhoon signal did not appear at the initial time. As verified against various observations, the control experiment captured reasonably well the evolution of the storm and the spatial distribution and evolution of the precipitation, whereas the remote precipitation in Japan was largely suppressed in the no-typhoon experiment, indicting the significant far-reaching effects of Typhoon Songda. Songda enhanced the remote precipitation in Japan mainly through northward moisture transport into the preconditioned precipitation region by its outer circulation. The orographic forcing of the central mountains in Japan played a small role compared with Typhoon Songda in this extreme precipitation event.

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Medicane Ianos: a comparative study between WRF model and satellite measurements

10.5194/ems2021-297 ◽

2021 ◽

Author(s):

Stefano Federico ◽

Albert Comellas Prat ◽

Rosa Claudia Torcasio ◽

Leo Pio D'Adderio ◽

Stefano Dietrich ◽

...

Keyword(s):

Water Content ◽

Prediction Models ◽

Quantitative Estimation ◽

Weather Prediction ◽

Wrf Model ◽

Heavy Precipitation ◽

Liquid Water Content ◽

Correct Prediction ◽

Satellite Measurements ◽

North West

<p>On September 14th, 2020 a depression originated on the Libyan coasts generated a Mediterranean tropical-like cyclone (hereafter referred to as Medicane Ianos), which moved northward until it hit, with its northernmost cloud bands, the southern Italian coasts and finally bent towards Greece, where it made landfall on September 18th, 2020. Heavy precipitation and flash flooding were reported, associated to huge damages to railway, houses, and four casualties.</p><p>The correct prediction, as much as possible, of the trajectory and the intensity of these events is fundamental to prevent risks to infrastructures, natural landscapes, and people. One of the ways to evaluate the performances of the numerical weather prediction models is the comparison with satellite measurements. In particular, the mature phase of Medicane Ianos, as predicted by the weather research and forecasting (WRF) model, has been compared, for the first time, with the Global Precipitation Measurement mission Core Observatory (GPM-CO) active and passive measurements. Different microphysics schemes were used in order to investigate which is the most suitable to achieve the best forecast of Medicane Ianos considering different parameters as depression localization, reflectivity, rainfall rate, integrated liquid and ice content. The results show that all the schemes identified the precipitation bands structure of the Medicane overestimating the vertical extent of the convective structures. At the same time, all the schemes predicted an excessive columnar ice water content if compared to the one estimated from satellite measurements. It has to be highlighted that the overestimation is marked on the western precipitation bands of the Medicane eye, while a better agreement is obtained for the northern bands. Similar results are obtained for columnar liquid water content, even if the quantitative estimation is closer to the GPM measurements. Finally, all the schemes located the Medicane circulation center further north-west of its actual position.</p>

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

Remote Sensing ◽

10.3390/rs11080973 ◽

2019 ◽

Vol 11 (8) ◽

pp. 973 ◽

Cited By ~ 1

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.

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Predictability of Heavy Precipitation in the Waikato River Basin of New Zealand

Monthly Weather Review ◽

10.1175/2010mwr3137.1 ◽

2011 ◽

Vol 139 (7) ◽

pp. 2184-2197 ◽

Cited By ~ 13

Author(s):

Stacey Dravitzki ◽

James McGregor

Keyword(s):

New Zealand ◽

River Basin ◽

Heavy Rainfall ◽

Model Simulation ◽

Wrf Model ◽

Heavy Precipitation ◽

Probability Of Detection ◽

Weather System ◽

Gfs Model ◽

Waikato River

Abstract This paper investigates the predictability of heavy precipitation in the economically important Waikato River basin of New Zealand. A 2-yr archive of Global Forecast System (GFS) model data to +180 h for the period August 2005–August 2007 forms the basis of the study. GFS model predictions of precipitation are compared to surface measurements from 22 stations in and around the river basin. Categorical hit rate and bias, threat score, false-alarm ratio, probability of detection, RMSE, skill score, and mean error are all plotted as a function of model lead time for the 2-yr period. The general synoptic structures of heavy precipitation events are often identified at long lead times. An example is shown for January 2006 when heavy rainfall is identified by GFS at +150 h, accurate location and timing is given at +48 h. Precipitation amounts are systematically underpredicted by GFS and the spatial distribution of rainfall is limited by model resolution. The value of GFS lies in its ability to provide early warning of potential heavy precipitation. Three heavy rainfall events were selected for higher-resolution Weather Research and Forecasting (WRF) model simulation. The WRF model produced more realistic geographical distributions of precipitation but cannot compensate for errors in the global model. WRF underpredicts precipitation for all three cases; the best simulation generates 92% of observed precipitation. Unobserved spurious convective rainfall can be generated by the WRF model following the passage of a frontal weather system. A possible mechanism is suggested that links this to underpredicted frontal rainfall.

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Impacts of the Madden-Julian Oscillation (MJO) on rainfall in Sri Lanka

MAUSAM ◽

10.54302/mausam.v71i3.39 ◽

2021 ◽

Vol 71 (3) ◽

pp. 405-422

Author(s):

JAYAWARDENA I M SHIROMANI PRIYANTHIKA ◽

WHEELER MATTHEW C ◽

SUMATHIPALA W L ◽

BASNAYAKE B R S B

Keyword(s):

Sri Lanka ◽

Large Scale ◽

Daily Rainfall ◽

Extreme Rainfall ◽

Southwest Monsoon ◽

Boreal Winter ◽

Northeast Monsoon ◽

Madden Julian Oscillation ◽

Extreme Rainfall Events ◽

Rainfall Events

The influence of the Madden Julian Oscillation (MJO) on rainfall in Sri Lanka (SL) is examined based on 30 years of daily station data from 1981-2010. Composites are constructed for each of the eight phases of the MJO defined with the Real-time Multivariate MJO (RMM) index, using daily rainfall data from 44 stations over SL for four climatic seasons and comparing to similar results from a satellite-based rainfall product. Composites of lower tropospheric wind and convective anomaly are also investigated in order to examine how the local rainfall anomalies are associated with large-scale circulations. The greatest impact of the MJO on rainfall over SL occurs in the Second Inter-Monsoon (SIM) and Southwest Monsoon (SWM) seasons. Enhanced rainfall generally occurs over SL during RMM phases 2 and 3 when the MJO convective envelop is located in the Indian Ocean and conversely suppressed rainfall in phases 6 and 7. This rainfall impact is due to the direct influence of the MJO’s tropical convective anomalies and associated low-level circulations in the vicinity of SL. In contrast, the MJO influence during the Northeast Monsoon (NEM) season is slightly less than during the SWM and SIM seasons as a result of the southward shift of the MJO convective envelop during boreal winter. Occurrence of extreme rainfall events is most frequent during phase 2 in First Inter-Monsoon (FIM) phases 2 and 3 in SWM, phases 1, 2 and 3 in SIM and phases 2 and 3 in NEM seasons. The analysis of this study provides a useful reference of when and where the MJO has significant impacts on rainfall as well as extreme rainfall events during four climatic seasons in SL. This information can be used along with accurately predicted MJO phase by dynamical or statistical models, to improve extended range forecasting in SL.

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