scholarly journals Using SEEPS with a TRMM-derived climatology to assess global NWP precipitation forecast skill

2021 ◽  
Author(s):  
Rachel C. North ◽  
Marion P. Mittermaier ◽  
Sean F. Milton
Keyword(s):  
Probability Space ◽  
Weather Prediction ◽  
Grid Point ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Tropical Rainfall Measurement Mission ◽  
Forecast Performance ◽  
Rain Gauges ◽  
Global Coverage ◽  
Global Precipitation

AbstractMonitoring precipitation forecast skill in global Numerical Weather Prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products on the other hand provide near-global coverage at a resolution of ~10-25 km, but limitations on data quality (e.g. biases) must be accommodated. In this paper the Stable Equitable Error in Probability Space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measurement Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology, and applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS is explored by comparing the gauge scores, based on extracting the nearest model grid point, to those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ~6000 locations (dictated by the number of gauges available globally) to all land points within the TRMM region of 50°N and 50°S. Finally, the forecast performance over the oceanic areas is compared to performance over land. Whilst the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

scholarly journals Assessment of Radio Occultation Observations from the COSMIC-2 Mission with a Simplified Observing System Simulation Experiment Configuration

2017 ◽  
Vol 145 (9) ◽  
pp. 3581-3597 ◽  
Author(s):  
L. Cucurull ◽  
R. Li ◽  
T. R. Peevey
Keyword(s):  
Radio Occultation ◽  
Weather Forecasting ◽  
System Simulation ◽  
Weather Prediction ◽  
Forecast Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Global Coverage ◽  
Prediction Systems ◽  
Observing System Simulation Experiment

The mainstay of the global radio occultation (RO) system, the COSMIC constellation of six satellites launched in April 2006, is already past the end of its nominal lifetime and the number of soundings is rapidly declining because the constellation is degrading. For about the last decade, COSMIC profiles have been collected and their retrievals assimilated in numerical weather prediction systems to improve operational weather forecasts. The success of RO in increasing forecast skill and COSMIC’s aging constellation have motivated planning for the COSMIC-2 mission, a 12-satellite constellation to be deployed in two launches. The first six satellites (COSMIC-2A) are expected to be deployed in December 2017 in a low-inclination orbit for dense equatorial coverage, while the second six (COSMIC-2B) are expected to be launched later in a high-inclination orbit for global coverage. To evaluate the potential benefits from COSMIC-2, an earlier version of the NCEP’s operational forecast model and data assimilation system is used to conduct a series of observing system simulation experiments with simulated soundings from the COSMIC-2 mission. In agreement with earlier studies using real RO observations, the benefits from assimilating COSMIC-2 observations are found to be most significant in the Southern Hemisphere. No or very little gain in forecast skill is found by adding COSMIC-2A to COSMIC-2B, making the launch of COSMIC-2B more important for terrestrial global weather forecasting than that of COSMIC-2A. Furthermore, results suggest that further improvement in forecast skill might better be obtained with the addition of more RO observations with global coverage and other types of observations.

scholarly journals Assessing the impact of Argo floats temperature measurements on the numerical weather prediction forecast skill

2019 ◽  
Vol 20 (2) ◽  
pp. 331 ◽  
Author(s):  
GEORGE VARLAS ◽  
PETROS KATSAFADOS ◽  
GERASIMOS KORRES ◽  
ANASTASIOS PAPADOPOULOS
Keyword(s):  
Boundary Conditions ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Forecast Skill ◽  
Temperature Measurements ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Argo Floats ◽  
The Impact

The forecast skill of numerical weather prediction (NWP) models relies, among other factors such as the prediction itself and the assimilation scheme, on the accuracy of the observations utilized in the assimilation systems for the production of initial and boundary conditions. One of the most crucial parameters in weather forecasting is the sea surface temperature (SST). In the majority of NWP models, the initial and lower boundary conditions involve gridded (SST) analyses which consist of data obtained by buoys, ships and satellites. The main aim of this study is to integrate Argo temperature measurements in gridded SST analyses and to assess their impact on the forecast skill of a limited area atmospheric model. Argo floats are “state-of-the-art” oceanographic instruments producing high-quality temperature profiles for the ice-free ocean. In this study, Argo temperatures are incorporated into gridded SST fields without applying any smoothing method in order to directly assess the impact of Argo temperatures on numerical weather prediction. Their impact is assessed under intense weather cyclonic conditions at the Mediterranean Sea by performing two sensitivity simulations either incorporating or not Argo temperatures into gridded SST fields used in the generation of the initial and lower boundary conditions. The results indicate that the inclusion of Argo-measured near-surface temperatures in the lower boundary condition modifies the surface heat fluxes, thus affecting mean sea level pressure and precipitation. In particular, an overall improvement of the precipitation forecast skill up to 3% has been demonstrated. Moreover, the incorporation of Argo temperatures affects the simulated track and intensity of the cyclone over the Balkan Peninsula.

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 Precipitation forecast skill of numerical weather prediction models and radar nowcasts

2005 ◽  
Vol 32 (14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Charles Lin ◽  
Slavko Vasić ◽  
Alamelu Kilambi ◽  
Barry Turner ◽  
Isztar Zawadzki
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

scholarly journals Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

2019 ◽  
Vol 34 (3) ◽  
pp. 701-714 ◽  
Author(s):  
Shunji Kotsuki ◽  
Kenta Kurosawa ◽  
Shigenori Otsuka ◽  
Koji Terasaki ◽  
Takemasa Miyoshi
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Optimal Weights ◽  
Threat Score ◽  
Optimal Weighting ◽  
Viable Approach ◽  
Global Precipitation

Abstract Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.

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.

scholarly journals Commercial-Aircraft-Based Observations for NWP: Global Coverage, Data Impacts, and COVID-19

2020 ◽  
Vol 59 (11) ◽  
pp. 1809-1825
Author(s):  
Eric P. James ◽  
Stanley G. Benjamin ◽  
Brian D. Jamison
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Additional Experiment ◽  
Commercial Aircraft ◽  
Aircraft Observation ◽  
Weather Observations ◽  
Aircraft Observations ◽  
Global Coverage ◽  
Commercial Flight

AbstractWeather observations from commercial aircraft constitute an essential component of the global observing system and have been shown to be the most valuable observation source for short-range numerical weather prediction (NWP) systems over North America. However, the distribution of aircraft observations is highly irregular in space and time. In this study, we summarize the recent state of aircraft observation coverage over the globe and provide an updated quantification of its impact upon short-range NWP forecast skill. Aircraft observation coverage is most dense over the contiguous United States and Europe, with secondary maxima in East Asia and Australia/New Zealand. As of late November 2019, 665 airports around the world had at least one daily ascent or descent profile observation; 400 of these come from North American or European airports. Flight reductions related to the COVID-19 pandemic have led to a 75% reduction in aircraft observations globally as of late April 2020. A set of data denial experiments with the latest version of the Rapid Refresh NWP system for recent winter and summer periods quantifies the statistically significant positive forecast impacts of assimilating aircraft observations. A special additional experiment excluding approximately 80% of aircraft observations reveals a reduction in forecast skill for both summer and winter amounting to 30%–60% of the degradation seen when all aircraft observations are excluded. These results represent an approximate quantification of the NWP impact of COVID-19-related commercial flight reductions, demonstrating that regional NWP guidance is degraded as a result of the decreased number of aircraft observations.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

scholarly journals Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

2017 ◽  
Vol 21 (9) ◽  
pp. 4841-4859 ◽  
Author(s):  
Sean W. D. Turner ◽  
James C. Bennett ◽  
David E. Robertson ◽  
Stefano Galelli
Keyword(s):  
System Performance ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Strong Relationship ◽  
Forecast Skill ◽  
Severe Drought ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Sensitivity Assessment ◽  
Seasonal Streamflow

Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

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