scholarly journals Spatial Bias in Medium-Range Forecasts of Heavy Precipitation in the Sacramento River Basin: Implications for Water Management

2020 ◽  
Vol 21 (7) ◽  
pp. 1405-1423
Author(s):  
Zachary P. Brodeur ◽  
Scott Steinschneider
Keyword(s):  
Forecast Error ◽  
Heavy Precipitation ◽  
Sacramento River ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Synoptic Scale ◽  
Basin Scale ◽  
Medium Range ◽  
Precipitation Events ◽  
Range Forecasts

AbstractForecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.

scholarly journals Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze–Huai River Valley in Summer 2016

2018 ◽  
Vol 33 (1) ◽  
pp. 221-238 ◽  
Author(s):  
Baiquan Zhou ◽  
Panmao Zhai ◽  
Ruoyun Niu
Keyword(s):  
Statistical Downscaling ◽  
Extreme Precipitation ◽  
Heavy Precipitation ◽  
Lead Times ◽  
Extreme Precipitation Events ◽  
Huai River ◽  
Ensemble Mean ◽  
Medium Range ◽  
Forecast Models ◽  
Precipitation Events

Abstract Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze–Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (≥25 mm day−1) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models’ performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4–10 days.

scholarly journals Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10852
Author(s):  
Gregor Skok ◽  
Doruntina Hoxha ◽  
Žiga Zaplotnik
Keyword(s):  
Neural Networks ◽  
Lead Time ◽  
Forecast Error ◽  
Lead Times ◽  
Profile Measurement ◽  
Profile Data ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Range Forecasts

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.

scholarly journals A Multiscale Evaluation of Multisensor Quantitative Precipitation Estimates in the Russian River Basin

2019 ◽  
Vol 20 (3) ◽  
pp. 447-466 ◽  
Author(s):  
Janice L. Bytheway ◽  
Mimi Hughes ◽  
Kelly Mahoney ◽  
Robert Cifelli
Keyword(s):  
Reanalysis Data ◽  
Small Scale ◽  
Synoptic Scale ◽  
Unique Challenge ◽  
Basin Scale ◽  
Variable Nature ◽  
Precipitation Estimates ◽  
Russian River ◽  
Quantitative Precipitation Estimates ◽  
Precipitation Events

Abstract The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin’s location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar.

Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan

2020 ◽  
Author(s):  
Jing-Shan Hong ◽  
Wen-Jou Chen ◽  
Ying-Jhen Chen ◽  
Siou-Ying Jiang ◽  
Chin-Tzu Fong
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Precipitation Forecast ◽  
Preliminary Evaluation ◽  
Satellite Mission ◽  
Background Error ◽  
Weather Bureau ◽  
Central Weather Bureau ◽  
Precipitation Events

<p>The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N.  </p><p>In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.</p>

scholarly journals On reliability analysis of multi-categorical forecasts

2008 ◽  
Vol 15 (4) ◽  
pp. 661-673 ◽  
Author(s):  
J. Bröcker
Keyword(s):  
Reliability Analysis ◽  
Goodness Of Fit ◽  
Necessary Condition ◽  
Lead Times ◽  
Temperature Anomalies ◽  
Probability Score ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Rank Histogram ◽  
Range Forecasts

Abstract. Reliability analysis of probabilistic forecasts, in particular through the rank histogram or Talagrand diagram, is revisited. Two shortcomings are pointed out: Firstly, a uniform rank histogram is but a necessary condition for reliability. Secondly, if the forecast is assumed to be reliable, an indication is needed how far a histogram is expected to deviate from uniformity merely due to randomness. Concerning the first shortcoming, it is suggested that forecasts be grouped or stratified along suitable criteria, and that reliability is analyzed individually for each forecast stratum. A reliable forecast should have uniform histograms for all individual forecast strata, not only for all forecasts as a whole. As to the second shortcoming, instead of the observed frequencies, the probability of the observed frequency is plotted, providing and indication of the likelihood of the result under the hypothesis that the forecast is reliable. Furthermore, a Goodness-Of-Fit statistic is discussed which is essentially the reliability term of the Ignorance score. The discussed tools are applied to medium range forecasts for 2 m-temperature anomalies at several locations and lead times. The forecasts are stratified along the expected ranked probability score. Those forecasts which feature a high expected score turn out to be particularly unreliable.

scholarly journals Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations

2020 ◽  
Author(s):  
Alexane Lovat ◽  
Béatrice Vincendon ◽  
Véronique Ducrocq
Keyword(s):  
Emergency Services ◽  
Flash Flood ◽  
Weather Prediction ◽  
Coupled System ◽  
Heavy Precipitation ◽  
Point Of View ◽  
Lead Times ◽  
Numerical Weather ◽  
Weather Model ◽  
Precipitation Events

Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to six hours, which is a relevant lead time for emergency services in crisis time. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occured in southeastern France. Precipitation forecasts are evaluated at a 15 min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrologic point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15/1 h 30 of forecast, the performance of PIAF is higher than AROME-NWC. For longer lead times (up to 3 h) their performance are equivalent in general. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash-flood simulations, forced by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash-flood forecasting in terms of peak intensity, timing, and first rise of discharge, with an anticipation of these phenomena that can reach several hours.

scholarly journals High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016

2018 ◽  
Vol 19 (8) ◽  
pp. 1289-1304 ◽  
Author(s):  
Bong-Chul Seo ◽  
Felipe Quintero ◽  
Witold F. Krajewski
Keyword(s):  
High Resolution ◽  
Lead Time ◽  
Performance Metrics ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Flood Prediction ◽  
Forecast Lead Time ◽  
Basin Scale ◽  
Hydrologic Prediction ◽  
Map Analysis

Abstract This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling–Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1–3-h lead times and starts increasing with 4–6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.

scholarly journals Diagnosis of the Source and Evolution of Medium-Range Forecast Errors for Extratropical Cyclone Joachim

2016 ◽  
Vol 31 (4) ◽  
pp. 1197-1214 ◽  
Author(s):  
William S. Lamberson ◽  
Ryan D. Torn ◽  
Lance F. Bosart ◽  
Linus Magnusson
Keyword(s):  
Western Europe ◽  
Forecast Error ◽  
Extratropical Cyclone ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Ensemble Prediction System ◽  
Medium Range Forecast ◽  
Medium Range ◽  
The Difference ◽  
Range Forecasts

Abstract Medium-range forecasts for Cyclone Joachim, an extratropical cyclone that impacted western Europe on 16 December 2011, consistently predicted a high-impact intense cyclone; however, these forecasts failed to verify. The potential source and propagation of forecast errors for this case are diagnosed from the 51-member European Centre for Medium-Range Forecasts Ensemble Prediction System initialized 5 days prior to the cyclone’s landfall. Ensemble members are subdivided into two groups: one that contained the eight members that had the most accurate forecast of Joachim and, the other, the eight members that predicted the most intense cyclone. Composite differences between these two subgroups indicate that the difference between these forecasts originate in tropopause-based subsynoptic waves along a deep trough in the eastern Pacific. These errors move eastward over a northern stream ridge centered on the west coast of North America and modulate the evolution of a trough that dives equatorward out of Canada and is associated with the development of Joachim. Forecast error calculations and relaxation experiments indicate that reducing forecast errors associated with these subsynoptic features leads to more accurate forecasts. These results present further evidence that subsynoptic errors, especially those originating in the warm sector of a cyclone, can be a significant source of downstream forecast errors.

scholarly journals Track and Circulation Analysis of Tropical and Extratropical Cyclones that Cause Strong Precipitation and Streamflow Events in the New York City Watershed

2018 ◽  
Vol 19 (6) ◽  
pp. 1027-1042 ◽  
Author(s):  
Katherine L. Towey ◽  
James F. Booth ◽  
Allan Frei ◽  
Mark R. Sinclair
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Warm Season ◽  
Heavy Precipitation ◽  
Specific Humidity ◽  
Extratropical Cyclones ◽  
Cool Season ◽  
Basin Scale ◽  
Precipitation Events

Abstract The top 100 basin-scale 1-day precipitation, multiday precipitation, and 1-day streamflow events from 1950 to 2012 are examined for the Ashokan reservoir, a key water source for New York City. Through a cyclone association algorithm, extratropical cyclones (ETCs) are found to be associated with the majority of the top 100 precipitation and streamflow events. Tropical cyclones (TCs) generate the second-most top 100 one-day and multiday precipitation events, with more than two-thirds of these TCs having undergone extratropical transition. Furthermore, TCs that pass over the region are approximately 7 and 4 times more likely to generate a top 100 one-day precipitation and one-day streamflow event, respectively, than ETCs. Lagrangian cyclone track analysis shows cool season ETCs take a more meridional path compared to warm season ETCs. A composite analysis shows that for the top 100 one-day precipitation events, ETCs have relatively less moisture but stronger upper-level support than TCs. Due in part to TCs, heavy precipitation events occur more often in the warm season, whereas high streamflow events occur mainly in the cool season. Despite this difference, approximately 43% of the top 100 events, which represent many of the very strongest events, overlap for all three metrics. While high temperature and specific humidity anomalies accompany all top 100 events, the magnitude of the anomalies is greatest for isolated streamflow events. This analysis provides a reference to forecasters and water managers regarding the relative and synoptic-scale behavior of different storm types for isolated and concurrent precipitation and streamflow events.

scholarly journals Study on Scale-Selective Initial Perturbation for Regional Ensemble Forecast

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 285
Author(s):  
Yu Xia ◽  
Hanbin Zhang ◽  
Jing Chen
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Initial Perturbation ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Comparison Results ◽  
Sensitivity Tests ◽  
Ensemble Transform Kalman Filter ◽  
Selection Of

To improve the skills of the regional ensemble forecast system (REFS), a modified ensemble transform Kalman filter (ETKF) initial perturbation strategy was developed. First, sensitivity tests were conducted to investigate the influence of the perturbation scale on the ensemble spread growth and forecast skill. In addition, the scale characteristic of the forecast error was analyzed based on the results of these tests, and a new initial condition perturbation method was developed through scale-selection of the ETKF perturbations, namely, ETKF-SS (scale-selective ETKF). The performances of the ETKF-SS scheme and the original ETKF (hereinafter referred to as ETKF) scheme were tested and compared. The results showed that the large-scale perturbations were much easier to grow than the original ETKF perturbations. In addition, scale analysis of the forecast error showed that the large-scale errors showed significant growth at the upper levels, while the small and meso-scale errors grew fast at the lower levels. The comparison results of the ETKF-SS and the ETKF showed that the ETKF-SS perturbations had more obvious growth than the ETKF perturbations, and the ETKF-SS perturbations in the short-term forecast lead times were more precise than the ETKF perturbations. The ensemble forecast verification results showed that the ETKF-SS ensemble had a larger spread and smaller root mean square error than the ETKF at short forecast lead times, while the probabilistic scores of the ETKF-SS also outperformed those of the ETKF method. In addition, the ETKF-SS ensemble can provide a better precipitation forecast than the ETKF.

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