scholarly journals The Extreme Precipitation Forecast Table: improving situational awareness when heavy rain is a threat

2020 ◽  
pp. 93-104
Author(s):  
Diana R. Stovern ◽  
James A. Nelson ◽  
Stan Czyzyk ◽  
Mark Klein ◽  
Katie Landry-Guyton ◽  
...  
Keyword(s):  
Mississippi River ◽  
Extreme Precipitation ◽  
Situational Awareness ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Weather Forecast ◽  
Heavy Rain ◽  
Precipitation Forecast ◽  
Lower Mississippi River ◽  
Quantitative Precipitation Estimates

A collaborative team of Science and Operations Officers from the National Weather Service (NWS) Weather Forecast Offices (WFOs), hydrologists from the Lower Mississippi River Forecast Center (LMRFC), and management from the Weather Prediction Center (WPC) worked together to develop and transition a tool into NWS operations called the Extreme Precipitation Forecast Table (EPFT). The EPFT was designed to help NWS forecasters improve their situational awareness (SA) when heavy rainfall threatens their county warning area. The EPFT compares Quantitative Precipitation Forecasts (QPF) to Average Recurrence Intervals (ARIs) from the NOAA Atlas-14 to alert forecasters to the potential for climatologically significant and extreme rainfall. A counterpart to the EPFT, called the Extreme Precipitation Assessment Table (EPAT), compares observed precipitation (i.e., Quantitative Precipitation Estimates [QPE]) to inform forecasters as to the climatological significance of impactful rain events. This paper presents cases demonstrating the usefulness of the EPFT and EPAT in helping forecasters improve their SA in real-time operational settings when heavy rain was a threat.

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 Development of Verification Methodology for Extreme Weather Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Hong Guan ◽  
Yuejian Zhu
Keyword(s):  
Extreme Precipitation ◽  
Weather Forecast ◽  
Extreme Weather ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Forecast System ◽  
Extreme Precipitation Events ◽  
Cold Events ◽  
Extreme Cold ◽  
Verification Methodology

Abstract In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

Ensemble-Based Analysis of the May 2010 Extreme Rainfall in Tennessee and Kentucky

2014 ◽  
Vol 142 (1) ◽  
pp. 222-239 ◽  
Author(s):  
Samantha L. Lynch ◽  
Russ S. Schumacher
Keyword(s):  
Mississippi River ◽  
Flash Flood ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Extreme Rainfall Event ◽  
Ensemble Forecasts ◽  
Surface Cyclone ◽  
Upper Level ◽  
Surrounding Areas

Abstract From 1 to 3 May 2010, persistent heavy rainfall occurred in the Ohio and Mississippi River valleys due to two successive quasi-stationary mesoscale convective systems (MCSs), with locations in central Tennessee accumulating more than 483 mm of rain, and the city of Nashville experiencing a historic flash flood. This study uses operational global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) to diagnose atmospheric processes and assess forecast uncertainty in this event. Several ensemble analysis methods are used to examine the processes that led to the development and maintenance of this precipitation system. Differences between ensemble members that correctly predicted heavy precipitation and those that did not were determined, in order to pinpoint the processes that were favorable or detrimental to the system's development. Statistical analysis was used to determine how synoptic-scale flows were correlated to 5-day area-averaged precipitation. The precipitation throughout Nashville and the surrounding areas occurred ahead of an upper-level trough located over the central United States. The distribution of precipitation was found to be closely related to the strength of this trough and an associated surface cyclone. In particular, when the upper-level trough was elongated, the surface cyclone remained weaker with a narrower low-level jet from the south. This caused the plume of moisture from the Caribbean Sea to be concentrated over Tennessee and Kentucky, where, in conjunction with focused ascent, heavy rain fell. Relatively small differences in the wind and pressure fields led to important differences in the precipitation forecasts and highlighted some of the uncertainties associated with predicting this extreme rainfall event.

Extreme Quantitative Precipitation Forecast Performance at the Weather Prediction Center from 2001 to 2011

2014 ◽  
Vol 29 (4) ◽  
pp. 894-911 ◽  
Author(s):  
Ellen M. Sukovich ◽  
F. Martin Ralph ◽  
Faye E. Barthold ◽  
David W. Reynolds ◽  
David R. Novak
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Forecast Performance ◽  
Extreme Precipitation Events ◽  
Quantitative Precipitation Forecast

Abstract Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA’s Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP’s Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAEcond). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events.

scholarly journals Scale-Dependent Uncertainties in Global QPFs and QPEs from NWP Model and Satellite Fields

2010 ◽  
Vol 11 (1) ◽  
pp. 139-155 ◽  
Author(s):  
C. Lu ◽  
H. Yuan ◽  
E. I. Tollerud ◽  
N. Wang
Keyword(s):  
Large Scale ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Global Model ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
Satellite Precipitation ◽  
Model Based ◽  
Nwp Model ◽  
Global Precipitation

Abstract Global precipitation forecasts from numerical weather prediction (NWP) models can be verified using the near-global coverage of satellite precipitation retrievals. However, inaccuracies in satellite precipitation analyses complicate the interpretation of forecast errors that result from verification of an NWP model against satellite observations. In this study, assessments of both a global quantitative precipitation estimate (QPE) from a satellite precipitation product and corresponding global quantitative precipitation forecast (QPF) from a global NWP model are conducted using available global land-based gauge data. A scale decomposition technique is devised, coupled with seasonal and spatial classifications, to evaluate these inaccuracies. The results are then analyzed in context with various physical precipitation systems, including heavy monsoonal rains, light Mediterranean winter rains, and North American convective-related and midlatitude cyclone–related precipitation. In general, global model results tend to consistently overforecast rainfall, whereas satellite measurements present a mixed pattern, underestimating many large-scale precipitation systems while overestimating many convective-scale precipitation systems. Both global model QPF and satellite-retrieved QPE showed better correlation scores in large-scale precipitation systems when verified with gauge measurements. In this case, model-based QPF tends to outperform satellite-retrieved QPE. At convective scales, there are significant drops in both model QPF and satellite QPE correlation scores, but satellite QPE performs slightly better than model QPF. These general results also showed regional and seasonal variation. For example, in tropical monsoon systems, satellite QPE tended to outperform model-based QPF at both scales. Overall, the results suggest potential improvements for both satellite estimates and weather forecast systems, in particular as applied to global precipitation forecasts.

Landslide Hazard and Exposure Modeling for Situational Awareness and Response in Rio de Janeiro

2021 ◽  
Author(s):  
Dalia Kirschbaum ◽  
Felipe Mandarino ◽  
Raquel Fonseca ◽  
Ricardo D'Orsi ◽  
Robert Emberson ◽  
...  
Keyword(s):  
Real Time ◽  
Landslide Susceptibility ◽  
Rio De Janeiro ◽  
Situational Awareness ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Precipitation Forecast ◽  
Rainfall Thresholds ◽  
The City

<p>The city of Rio de Janeiro is situated within a coastal region with steep slopes, intense seasonal rainfall, and vulnerable populations located on marginal slopes. Landslides are a seasonal challenge within the city and proximate regions and increasing real-time awareness of the hazard and exposure is paramount to saving lives and mitigating damage. A local alerting system has been developed for the city that leverages a global landslide hazard assessment for situational awareness (LHASA) framework, developed by NASA, with local rainfall thresholds and landslide susceptibility information. The LHASA-Rio system uses a decision tree approach to first identify extreme rainfall based on a series of rainfall thresholds established by Geo-Rio (the City’s agency responsible for landslide hazards) for 1 hour, 1 day or 1 hour and 4 day thresholds. This is then coupled with information on landslide susceptibility also developed by the Geo-Rio team. The LHASA-Rio system has been running operationally since 2017 within the city to provide real-time, high resolution estimates of areas within the city at higher hazard at 15-minute intervals consistent with the rainfall gauge network distributed throughout the city. Results of the LHASA-Rio system indicate excellent performance for several case studies where extreme rainfall triggered landslides within the city over areas identified as high hazard zones by LHASA-Rio. The model has recently been updated to accommodate additional rainfall thresholds to differentiate moderate to very high and critical intensities. The modeling effort is also incorporating information on landslide exposure by connecting the hazard estimates to city-wide data on population, road networks and other infrastructure. The goal of this system is ultimately to provide key tools to emergency response teams, civil protection and other hazard monitoring organizations within Rio’s City Government in real-time and provide  actionable information for key communities, city management and planning. Future work of this system is the application of a regional precipitation forecast to improve the lead time.</p><p>This work has been done in partnership through an agreement established between NASA and the City of Rio de Janeiro in 2015 that was recently extended in 2020. This agreement seeks to support innovative efforts to better understand, anticipate, and monitor hazards and environmental issues, including heavy rainfall and landslides, urban flooding, air quality and water quality in and around the city. This collaboration leverages the unique attributes of NASA's satellite data and modeling frameworks and Rio de Janeiro's management and monitoring capabilities to improve awareness of how the city of Rio may be impacted by hazards and affected by climate change. If the success of this technology is demonstrated, other cities in the world with physiographic and socioeconomic characteristics similar to Rio de Janeiro may benefit by implementing, or strengthening, their own Early Warning Systems for landslides triggered by heavy rains using LHASA's open source algorithms and the experience gathered by the use of LHASA-Rio. This presentation highlights the achievements and advancements of the LHASA-Rio system and discusses lessons learned regarding the applications of the landslide modeling systems to advance decision-relevant science at the city level.</p>

scholarly journals “Dendrology” in Numerical Weather Prediction: What Random Forests and Logistic Regression Tell Us about Forecasting Extreme Precipitation

2018 ◽  
Vol 146 (6) ◽  
pp. 1785-1812 ◽  
Author(s):  
Gregory R. Herman ◽  
Russ S. Schumacher
Keyword(s):  
Logistic Regression ◽  
Extreme Precipitation ◽  
Weather Prediction ◽  
Principal Component ◽  
Companion Paper ◽  
Precipitation Forecast ◽  
Quantitative Precipitation Forecast ◽  
Forecast Models ◽  
Parameterized Model

Three different statistical algorithms are applied to forecast locally extreme precipitation across the contiguous United States (CONUS) as quantified by 1- and 10-yr average recurrence interval (ARI) exceedances for 1200–1200 UTC forecasts spanning forecast hours 36–60 and 60–84, denoted, respectively, day 2 and day 3. Predictors come from nearly 11 years of reforecasts from NOAA’s Second-Generation Global Ensemble Forecast System Reforecast (GEFS/R) model and derive from a variety of thermodynamic and kinematic variables that characterize the meteorological regime in addition to the quantitative precipitation forecast (QPF) output from the ensemble. In addition to encompassing nine different atmospheric fields, predictors also vary in space and time relative to the forecast point. Distinct models are trained for eight different hydrometeorologically cohesive regions of the CONUS. One algorithm supplies the GEFS/R predictors directly to a random forest (RF) procedure to produce extreme precipitation forecasts; the second also employs RFs, but the predictors instead undergo principal component analysis (PCA), and extracted leading components are supplied to the RF. In the last algorithm, dimension-reduced predictors are supplied to a logistic regression (LR) algorithm instead of an RF. A companion paper investigated the quality of the forecasts produced by these models and other RF-based forecast models. This study is an extension of that work and explores the internals of these trained models and what physical and statistical insights they reveal about forecasting extreme precipitation from a global, convection-parameterized model.

scholarly journals Validation of subgrid scale ensemble precipitation forecasts based on the ECMWF’s ecPoint Rainfall project

Időjárás ◽  
2021 ◽  
Vol 125 (3) ◽  
pp. 397-418
Author(s):  
Boglárka Tóth ◽  
István Ihász
Keyword(s):  
Extreme Precipitation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Time Range ◽  
Weather Forecasts ◽  
Extreme Precipitation Events ◽  
Operational Forecasting ◽  
Processed Products ◽  
Short Time

Nowadays, state-of-the-art numerical weather prediction models successfully predict the general weather characteristics several days ahead, but forecasting extreme precipitation is quite challenging even in the short time range. In the framework of the ecPoint Project, the European Centre for Medium-Range Weather Forecasts (ECMWF) developed a new innovative probabilistic post-processing tool which produces 4-day precipitation forecast as accurate as the raw ensemble forecast at day 1. In the framework of the scientific co-operation between ECMWF and the Hungarian Meteorological Service (OMSZ), we were invited to participate in the validation of the experimental products. Quasi operational post-processed products have been available since July 1, 2018. During our work, besides using different verification technics, a new ensemble meteogram was also developed which can support operational forecasters during extreme precipitation events. As a result of our work, products of the ecPoint Project have been included in the operational forecasting activity.

scholarly journals Extreme Rainfall and Flooding over Central Kenya Including Nairobi City during the Long-Rains Season 2018: Causes, Predictability, and Potential for Early Warning and Actions

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 472 ◽  
Author(s):  
Mary Kilavi ◽  
Dave MacLeod ◽  
Maurine Ambani ◽  
Joanne Robbins ◽  
Rutger Dankers ◽  
...  
Keyword(s):  
Risk Management ◽  
Early Warning ◽  
Flood Risk ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Flood Risk Management ◽  
Lead Times ◽  
Short Term ◽  
Rainfall Events

The Long-Rains wet season of March–May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden–Julian Oscillation (MJO) events in MJO phases 2–4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October–December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.

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