scholarly journals Evaluating medium-range forecast performance of regional-scale circulation patterns.

2021 ◽  
Author(s):  
Omon Obarein ◽  
Tyler Smith ◽  
Cameron Lee ◽  
Scott Sheridan
Keyword(s):  
Regional Scale ◽  
Forecast Performance ◽  
Medium Range Forecast ◽  
Circulation Patterns ◽  
Medium Range

An Analysis of the Accuracy of 120-h Predictions by the National Meteorological Center's Medium-Range Forecast Model

1994 ◽  
Vol 9 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Mary A. Bedrick ◽  
Anthony J. Cristaldi ◽  
Stephen J. Colucci ◽  
Daniel S. Wilks
Keyword(s):  
Forecast Model ◽  
Medium Range Forecast ◽  
Medium Range

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 The Impact of Convective Momentum Transport on Tropical Cyclone Track Forecasts Using the Emanuel Cumulus Parameterization

2007 ◽  
Vol 135 (4) ◽  
pp. 1195-1207 ◽  
Author(s):  
Timothy F. Hogan ◽  
Randal L. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Tropical Cyclones ◽  
Momentum Transport ◽  
Cumulus Parameterization ◽  
Transport Parameter ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Convective Momentum Transport ◽  
The Impact

Abstract The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

scholarly journals Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing

2016 ◽  
Vol 145 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Jakob W. Messner ◽  
Georg J. Mayr ◽  
Achim Zeileis
Keyword(s):  
Regression Coefficients ◽  
Maximum Temperature ◽  
Ensemble Forecasts ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Medium Range ◽  
Important Input ◽  
Boosting Algorithm ◽  
Input Variables

Abstract Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.

Precipitation and Temperature Forecast Performance at the Weather Prediction Center

2014 ◽  
Vol 29 (3) ◽  
pp. 489-504 ◽  
Author(s):  
David R. Novak ◽  
Christopher Bailey ◽  
Keith F. Brill ◽  
Patrick Burke ◽  
Wallace A. Hogsett ◽  
...  
Keyword(s):  
Short Range ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Extreme Rainfall Events ◽  
Forecast Performance ◽  
The Past ◽  
Medium Range

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

The Performance of a Medium-Range Forecast Model in Winter–Impact of Physical Parameterizations

1980 ◽  
Vol 108 (11) ◽  
pp. 1736-1773 ◽  
Author(s):  
A. Hollingsworth ◽  
K. Arpe ◽  
M. Tiedtke ◽  
M. Capaldo ◽  
H. Savijärvi
Keyword(s):  
Forecast Model ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Physical Parameterizations

scholarly journals The fast-acting “pulse” of Heinrich Stadial 3 in a mid-latitude boreal ecosystem

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Federica Badino ◽  
Roberta Pini ◽  
Paolo Bertuletti ◽  
Cesare Ravazzi ◽  
Barbara Delmonte ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Regional Scale ◽  
Internal Variability ◽  
Forest Expansion ◽  
Boreal Ecosystems ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Dry Conditions ◽  
Boreal Ecosystem ◽  
Fast Acting

Abstract A 3800 year-long radiocarbon-dated and highly-resolved palaeoecological record from Lake Fimon (N-Italy) served to investigate the effects of potential teleconnections between North Atlantic and mid-to-low latitudes at the transition from Marine Isotope Stage (MIS) 3 to 2. Boreal ecosystems documented in the Fimon record reacted in a sensitive way to millennial and sub-millennial scale Northern Hemisphere atmospheric circulation patterns. The high median time-resolution of 58 years allows the identification of five abrupt event-boundaries (i.e., main forest expansion and decline excursions) synchronous with the sharp stadial/interstadial (GS/GI) transitions within dating uncertainties. During Heinrich Stadial 3 (HS 3) we reconstruct more open and dry conditions, compared to the other GS, with a dominant regional scale fire signal. Linkages between local fires and climate-driven fuel changes resulted in high-magnitude fire peaks close to GI/GS boundaries, even exacerbated by local peatland conditions. Finally, palaeoecological data from the HS 3 interval unveiled an internal variability suggesting a peak between 30,425 and 29,772 cal BP (2σ error) which matches more depleted δ18O values in alpine speleothems. We hypothesise that this signal, broadly resembling that of other mid-latitudes proxies, may be attributed to the southward shift of the Northern Hemisphere storm tracks and the associated delayed iceberg discharge events as documented during other HS.

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