scholarly journals Sensitivity of Calibrated Week-2 Probabilistic Forecast Skill to Reforecast Sampling of the NCEP Global Ensemble Forecast System

2016 ◽  
Vol 31 (4) ◽  
pp. 1093-1107 ◽  
Author(s):  
Melissa H. Ou ◽  
Mike Charles ◽  
Dan C. Collins
Keyword(s):  
Sample Size ◽  
Ensemble Forecast ◽  
Forecast Skill ◽  
Large Sample Size ◽  
Forecast System ◽  
Temperature And Precipitation ◽  
Probabilistic Forecasts ◽  
Series Of Experiments ◽  
The Impact ◽  
Generate Model

Abstract CPC requires the reforecast-calibrated Global Ensemble Forecast System (GEFS) to support the production of their official 6–10- and 8–14-day temperature and precipitation forecasts. While a large sample size of forecast–observation pairs is desirable to generate the necessary model climatology and variances, and covariances to observations, sampling by reforecasts could be done to use available computing resources most efficiently. A series of experiments was done to assess the impact on calibrated forecast skill of using a smaller sample size than the current available reforecast dataset. This study focuses on the skill of week-2 probabilistic forecasts of the 7-day-mean 2-m temperature and accumulated precipitation. The tercile forecasts are expressed as being below-, near-, and above-normal temperature/median precipitation over the continental United States (CONUS). Calibration statistics were calculated using an ensemble regression technique from 25 yr of daily, 11-member GEFS reforecasts for 1986–2010, which were then used to postprocess the GEFS model forecasts for 2011–13. In assessing the skill of calibrated model output using a reforecast dataset with fewer years and ensemble members, and an ensemble run less frequently than daily, it was determined that reductions in the number of ensemble members to six or fewer and reductions in the frequency of reforecast runs from daily to once a week were achievable with minimal loss of skill. However, reducing the number of years of reforecasts to less than 25 resulted in a greater skill degradation. The loss of skill was statistically significant using only 18 yr of reforecasts from 1993 to 2010 to generate model statistics.

scholarly journals DAMPAK BPNT TERHADAP KONSUMSI RUMAH TANGGA DAN POLA KONSUMSI RUMAH TANGGA PENERIMA BPNT, KECAMATAN MENGWI

2021 ◽  
pp. 839
Author(s):  
Luh Ade Yumita Handriani ◽  
Sudarsana Arka
Keyword(s):  
Path Analysis ◽  
Sample Size ◽  
Direct Effect ◽  
Indirect Effect ◽  
Household Consumption ◽  
Consumption Patterns ◽  
Consumption Pattern ◽  
Large Sample Size ◽  
Analysis Techniques ◽  
The Impact

This study aims to analyze the impact of the BPNT program on household consumption and consumption patterns of BPNT recipient households in Mengwi District, Badung Regency. This research was conducted in Mengwi District, Badung Regency using a questionnaire distributed to respondents with a large sample size of 96 KPM. This study uses path analysis techniques to analyze the direct effect and Sobel test to analyze the indirect effect. Based on path analysis, the results of the study concluded that the BPNT variable had a positive and significant effect on the consumption of BPNT recipient households in Mengwi District, Badung Regency. The BPNT variable has no effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable has a negative and significant effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable did mediate the effect of the BPNT Program on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency

scholarly journals A Comparison of the Downstream Predictability Associated with ET and Baroclinic Cyclones

2017 ◽  
Vol 145 (11) ◽  
pp. 4651-4672 ◽  
Author(s):  
Ryan D. Torn
Keyword(s):  
Standard Deviation ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Pacific ◽  
Western North Pacific ◽  
Ensemble Forecast ◽  
Latent Heat Release ◽  
Propagation Characteristics ◽  
Forecast System ◽  
The Impact

The impact of the extratropical transition (ET) of tropical cyclones and baroclinic cyclogenesis in the western North Pacific (WNP), Atlantic, and southern Indian Ocean (SIO) basins on the predictability of the downstream midlatitude flow is assessed using 30 years of cases from the Global Ensemble Forecast System (GEFS) Reforecast, version 2. In all three basins, ET is associated with statistically larger 500-hPa geopotential height forecast standard deviation (SD) compared to the forecast climatology. The higher SD values originate from where the TC enters the midlatitudes and spread downstream at the group velocity of the associated wave packet. Of the three basins, WNP ET is associated with the largest amplitude and longest-lasting SD anomalies. Forecasts initialized 2–4 days prior to the onset of ET have larger SD anomalies compared to forecasts initialized during or after the onset of ET. By contrast, the region of positive SD anomaly associated with winter baroclinic cyclones is confined to the upstream trough, with fall cyclones exhibiting some downstream propagation characteristics similar to ET. The ET cases with the larger downstream SD anomaly are characterized by a more amplified ridge downstream of the TC as it enters the midlatitudes. By contrast, ET cases with an upstream trough, large TC position variability at the onset of ET, latent heat release, or upper-tropospheric PV advection by the irrotational wind are not characterized by significantly larger downstream SD compared to cases without an upstream trough or smaller values of these quantities.

scholarly journals Evaluation of the IRI'S “Net Assessment” Seasonal Climate Forecasts: 1997–2001

2003 ◽  
Vol 84 (12) ◽  
pp. 1761-1782 ◽  
Author(s):  
L. Goddard ◽  
A. G. Barnston ◽  
S. J. Mason
Keyword(s):  
Skill Score ◽  
Forecast Skill ◽  
Seasonal Temperature ◽  
Seasonal Climate ◽  
Prediction Tools ◽  
Temperature And Precipitation ◽  
Climate Forecasts ◽  
Probabilistic Forecasts ◽  
International Research Institute ◽  
The Individual

The International Research Institute for Climate Prediction (IRI) net assessment seasonal temperature and precipitation forecasts are evaluated for the 4-yr period from October–December 1997 to October–December 2001. These probabilistic forecasts represent the human distillation of seasonal climate predictions from various sources. The ranked probability skill score (RPSS) serves as the verification measure. The evaluation is offered as time-averaged spatial maps of the RPSS as well as area-averaged time series. A key element of this evaluation is the examination of the extent to which the consolidation of several predictions, accomplished here subjectively by the forecasters, contributes to or detracts from the forecast skill possible from any individual prediction tool. Overall, the skills of the net assessment forecasts for both temperature and precipitation are positive throughout the 1997–2001 period. The skill may have been enhanced during the peak of the 1997/98 El Niño, particularly for tropical precipitation, although widespread positive skill exists even at times of weak forcing from the tropical Pacific. The temporally averaged RPSS for the net assessment temperature forecasts appears lower than that for the AGCMs. Over time, however, the IRI forecast skill is more consistently positive than that of the AGCMs. The IRI precipitation forecasts generally have lower skill than the temperature forecasts, but the forecast probabilities for precipitation are found to be appropriate to the frequency of the observed outcomes, and thus reliable. Over many regions where the precipitation variability is known to be potentially predictable, the net assessment precipitation forecasts exhibit more spatially coherent areas of positive skill than most, if not all, prediction tools. On average, the IRI net assessment forecasts appear to perform better than any of the individual objective prediction tools.

scholarly journals Improving the Use of Hydrologic Probabilistic and Deterministic Information in Decision-Making

2021 ◽  
pp. 1-40
Author(s):  
Rachel Hogan Carr ◽  
Kathryn Semmens ◽  
Burrell Montz ◽  
Keri Maxfield
Keyword(s):  
Ground Truth ◽  
Ensemble Forecast ◽  
Forecast System ◽  
Science Study ◽  
Probabilistic Information ◽  
Social Science Study ◽  
Probabilistic Forecasts ◽  
Tremendous Amount ◽  
Hydrologic Prediction ◽  
Hydrologic Forecast

AbstractUncertainty is everywhere and understanding how individuals understand and use forecast information to make decisions given varying levels of certainty is crucial for effectively communicating risks and weather hazards. To advance prior research about how various audiences use and understand probabilistic and deterministic hydrologic forecast information, a social science study involving multiple scenario-based focus groups and surveys at four locations (Eureka, CA; Gunnison, CO; Durango, CO; Owego, NY) across the U.S. was conducted with professionals and residents. Focusing on the Hydrologic Ensemble Forecast System, the Advanced Hydrologic Prediction Service, and briefings, this research investigated how users tolerate divergence in probabilistic and deterministic forecasts and how deterministic and probabilistic river level forecasts can be presented simultaneously without causing confusion. This study found that probabilistic forecasts introduce a tremendous amount of new, yet valuable, information but can quickly overwhelm users based on how they are conveyed and communicated. Some were unaware of resources available, or how to find, sort and prioritize among all the data and information. Importantly, when presented with a divergence between deterministic and probabilistic forecasts, most sought out more information while some others reported diminished confidence in the products.Users in all regions expressed a desire to “ground-truth” the accuracy of probabilistic forecasts, understand the drivers of the forecasts, and become more familiar with them. In addition, a prototype probabilistic product that includes a deterministic forecast was tested, and suggestions for communicating probabilistic information through the use of briefing packages is proposed.

Assessment and Comparison of Subseasonal Bias and Forecast Skill in the Unified Forecast System (UFS) Benchmarks 3 and 5

2021 ◽  
Author(s):  
Keri Kodama ◽  
David Straus ◽  
James Kinter
Keyword(s):  
Systematic Error ◽  
Lead Time ◽  
Diabatic Heating ◽  
Model Development ◽  
Forecast Skill ◽  
Boreal Winter ◽  
Forecast System ◽  
Climate Forecast System ◽  
Temperature And Precipitation ◽  
Marked Diminution

<p>A series of reforecasts have been generated with prototype versions of the coupled Unified Forecast System (UFS) to evaluate progress in the model development. The forecast skill and biases of the UFS Prototypes 3 and 5 reforecast sets—called Benchmark 3 and Benchmark 5, respectively—are analyzed and compared with the NCEP Climate Forecast System version 2 (CFSv2) reforecasts from the Subseasonal Prediction Experiment (SubX). The evaluation focuses on surface variables typically provided in the subseasonal outlooks at weekly-averaged timescales, namely 2-meter air temperature, precipitation rate, and sea surface temperature. Additional assessment of the structure of the systematic error in total diabatic heating over three broad layers of the atmosphere (850-650 hPa, 650-450 hPa and 450-50 hPa) has been performed as a function of season and forecast lead. In terms of forecast skill, all models still experience a skill drop-off of varying degree by week 3. In general, however, the UFS prototypes considerably reduce the marked diminution of variability with lead time displayed in their predecessor, CFSv2. Moreover, the prototypes have reduced systematic error compared to CFSv2, particularly for 2-meter temperature and precipitation. A systematic overestimate of diabatic cooling is noted in the upper atmosphere (diabatic heating too negative compare to ERA-5 estimates) during boreal winter. </p>

scholarly journals Short-Range Ensemble Forecasts of Precipitation Type

2005 ◽  
Vol 20 (4) ◽  
pp. 609-626 ◽  
Author(s):  
Matthew S. Wandishin ◽  
Michael E. Baldwin ◽  
Steven L. Mullen ◽  
John V. Cortinas
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Precipitation Forecast ◽  
Initial Condition ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Precipitation Type ◽  
Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

scholarly journals Extreme Precipitation in Models: An Evaluation

2016 ◽  
Vol 31 (6) ◽  
pp. 1853-1879 ◽  
Author(s):  
Gregory R. Herman ◽  
Russ S. Schumacher
Keyword(s):  
Extreme Precipitation ◽  
Wrf Model ◽  
Ensemble Forecast ◽  
Forecast Skill ◽  
Forecast System ◽  
Severe Storms ◽  
The North ◽  
Extreme Precipitation Events ◽  
The U.S ◽  
Precipitation Events

Abstract A continental United States (CONUS)-wide framework for analyzing quantitative precipitation forecasts (QPFs) from NWP models from the perspective of precipitation return period (RP) exceedances is introduced using threshold estimates derived from a combination of NOAA Atlas 14 and older sources. Forecasts between 2009 and 2015 from several different NWP models of varying configurations and spatial resolutions are analyzed to assess bias characteristics and forecast skill for predicting RP exceedances. Specifically, NOAA’s Global Ensemble Forecast System Reforecast (GEFS/R) and the National Severe Storms Laboratory WRF (NSSL-WRF) model are evaluated for 24-h precipitation accumulations. The climatology of extreme precipitation events for 6-h accumulations is also explored in three convection-allowing models: 1) NSSL-WRF, 2) the North American Mesoscale 4-km nest (NAM-NEST), and 3) the experimental High Resolution Rapid Refresh (HRRR). The GEFS/R and NSSL-WRF are both found to exhibit similar 24-h accumulation RP exceedance climatologies over the U.S. West Coast to those found in observations and are found to be approximately equally skillful at predicting these exceedance events in this region. In contrast, over the eastern two-thirds of the CONUS, GEFS/R struggles to predict the predominantly convectively driven extreme QPFs, predicting far fewer events than are observed and exhibiting inferior forecast skill to the NSSL-WRF. The NSSL-WRF and HRRR are found to produce 6-h extreme precipitation climatologies that are approximately in accord with those found in the observations, while NAM-NEST produces many more RP exceedances than are observed across all of the CONUS.

Impact of Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data on Severe Convection Forecast in a Warn-on-Forecast System

2021 ◽  
Author(s):  
Yaping Wang ◽  
Nusrat Yussouf ◽  
Edward R. Mansell ◽  
Brian C. Matilla ◽  
Rong Kong ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
Radar Data ◽  
Forecast Skill ◽  
The Other ◽  
Storm Events ◽  
Forecast System ◽  
Short Term ◽  
Isolated Cells ◽  
Total Lightning ◽  
The Impact

AbstractThe Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the Ensemble Kalman Filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events, one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.

Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System

2020 ◽  
Vol 148 (5) ◽  
pp. 1829-1859
Author(s):  
Thomas A. Jones ◽  
Patrick Skinner ◽  
Nusrat Yussouf ◽  
Kent Knopfmeier ◽  
Anthony Reinhart ◽  
...  
Keyword(s):  
Current Model ◽  
Radar Data ◽  
Forecast Skill ◽  
Forecast System ◽  
Initial Attempt ◽  
Convective Initiation ◽  
Clear Sky ◽  
Object Based ◽  
Model Configuration ◽  
The Impact

Abstract The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.

scholarly journals On the Link between Summer Dry Bias over the U.S. Great Plains and Seasonal Temperature Prediction Skill in a Dynamical Forecast System

2019 ◽  
Vol 34 (4) ◽  
pp. 1161-1172 ◽  
Author(s):  
Constantin Ardilouze ◽  
Lauriane Batté ◽  
Bertrand Decharme ◽  
Michel Déqué
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Climate Models ◽  
Warm Season ◽  
Seasonal Temperature ◽  
Forecast System ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The U.S ◽  
Dry Bias

Abstract Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.

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