scholarly journals Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation

2020 ◽  
Author(s):  
Doug Richardson ◽  
Amanda S. Black ◽  
Didier P. Monselesan ◽  
Thomas S. Moore ◽  
James S. Risbey ◽  
...  
Keyword(s):  
Western Australia ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Archetypal Analysis ◽  
Weather Forecasts ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Murray Basin

AbstractSubseasonal forecast skill is not homogeneous in time, and prior assessment of the likely forecast skill would be valuable for end-users. We propose a method for identifying periods of high forecast confidence using atmospheric circulation patterns, with an application to southern Australia precipitation. In particular, we use archetypal analysis to derive six patterns, called archetypes, of daily 500 hPa geopotential height (Z500) fields over Australia. We assign Z500 reanalysis fields to the closest-matching archetype and subsequently link the archetypes to precipitation for three key regions in the Australian agriculture and energy sectors: the Murray Basin, Southwest Western Australia and Western Tasmania. Using a 20-year hindcast dataset from the European Centre for Medium-Range Weather Forecasts subseasonal-to-seasonal prediction system, we identify periods of high confidence as when hindcast Z500 fields closely match an archetype according to a distance criterion. We compare the precipitation hindcast accuracy during these confident periods compared to normal. Considering all archetypes, we show that there is greater skill during confident periods for lead times of less than 10 days in the Murray Basin and Western Tasmania, and for greater than six days in Southwest Western Australia, although these conclusions are subject to substantial uncertainty. By breaking down the skill results for each archetype individually, we highlight how skill tends to be greater than normal for those archetypes associated with drier-than-average conditions.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

scholarly journals The Madden–Julian Oscillation and Boreal Winter Forecast Skill: An Analysis of NCEP CFSv2 Reforecasts

2015 ◽  
Vol 28 (15) ◽  
pp. 6297-6307 ◽  
Author(s):  
Charles Jones ◽  
Abheera Hazra ◽  
Leila M. V. Carvalho
Keyword(s):  
Forecast Skill ◽  
Western Pacific ◽  
Boreal Winter ◽  
Lead Times ◽  
Madden Julian Oscillation ◽  
Main Mode ◽  
Weather Forecasts ◽  
Synoptic Variability ◽  
The Impact ◽  
The Western Pacific

Abstract The Madden–Julian oscillation (MJO) is the main mode of tropical intraseasonal variations and bridges weather and climate. Because the MJO has a slow eastward propagation and longer time scale relative to synoptic variability, significant interest exists in exploring the predictability of the MJO and its influence on extended-range weather forecasts (i.e., 2–4-week lead times). This study investigates the impact of the MJO on the forecast skill in Northern Hemisphere extratropics during boreal winter. Several 45-day forecasts of geopotential height (500 hPa) from NCEP Climate Forecast System version 2 (CFSv2) reforecasts are used (1 November–31 March 1999–2010). The variability of the MJO expressed as different amplitudes, durations, and recurrence (i.e., primary and successive events) and their influence on forecast skill is analyzed and compared against inactive periods (i.e., null cases). In general, forecast skill during enhanced MJO convection over the western Pacific is systematically higher than in inactive days. When the enhanced MJO convection is over the Maritime Continent, forecasts are lower than in null cases, suggesting potential model deficiencies in accurately forecasting the eastward propagation of the MJO over that region and the associated extratropical response. In contrast, forecasts are more skillful than null cases when the enhanced convection is over the western Pacific and during long, intense, and successive MJO events. These results underscore the importance of the MJO as a potential source of predictability on 2–4-week lead times.

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 Antarctic precipitation and its contribution to the global sea-level budget

1998 ◽  
Vol 27 ◽  
pp. 220-226 ◽  
Author(s):  
David H. Bromwich ◽  
Richard I. Cullather ◽  
Michael L. Van Woert
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Polar Regions ◽  
Weather Forecasts ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Order Of Magnitude ◽  
Global Sea Level ◽  
Antarctic Precipitation

Antarctic precipitation estimations derived from several new sources are examined in comparison to results found previously. The availability of analyzed atmospheric datasets has been a significant and beneficial tool for atmospheric and climate research for a broad range of research interests. This is particularly true for the polar regions, where the observational arrays are sparsely distributed. in high southern latitudes, a comprehensive assimilation of all available observations, including satellite data, is necessary for an accurate depiction of the atmospheric circulation. Recent st udies have found the operational analyses of the European Centre for Medium-range Weather Forecasts to be superior to those of other weather-forecasting centers in depicting the large-scale atmospheric circulation patterns over Antarctica. “Re-analysis” programs at major weather-forecasting centers have produced atmospheric numerical analyses using a “frozen” data-assimilation system. These projects have also derived precipitation and evaporation fields using an ensemble of short-term forecasts. From these new sources, Antarctic Ρ - E (precipitation minus evaporation/sublimation) is compared and evaluated against the long-term glaciological synthesis, as well as results from previous studies. The comparisons indicate significant regional disagreements exist between P — E from the re-analysis forecasts and the glaciological data. For the ensemble forecasting method, the continental-average evaporation is the largest area of uncertainty and differs by an order of magnitude between the rc-analysis datasets. This finding supports the use of the atmospheric moisture budget for determining P — E collectively in atmospheric diagnostic studies for Antarctica.

scholarly journals Analysis of a Short-Term and a Seasonal Precipitation Forecast Over Kenya

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1371
Author(s):  
Sara Miller ◽  
Vikalp Mishra ◽  
W. Lee Ellenburg ◽  
Emily Adams ◽  
Jason Roberts ◽  
...  
Keyword(s):  
Water Security ◽  
Seasonal Prediction ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Onset Date ◽  
Lead Times ◽  
Short Term ◽  
Starting Point ◽  
Agricultural Areas

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.

Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation in southern Australia

2020 ◽  
Author(s):  
Doug Richardson ◽  
James Risbey ◽  
Didier Monselesan
Keyword(s):  
A Priori ◽  
Forecast Model ◽  
Reanalysis Data ◽  
Lead Times ◽  
Conditional Distributions ◽  
Atmospheric Variability ◽  
Data Set ◽  
Typical Application ◽  
Model Skill ◽  
Circulation Patterns

<p>Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess <em>a priori</em> if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.</p><p>From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.</p><p>We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.</p>

scholarly journals The Stratospheric Extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and Its Impact on Tropospheric Forecasts

2012 ◽  
Vol 140 (6) ◽  
pp. 1924-1944 ◽  
Author(s):  
Martin Charron ◽  
Saroja Polavarapu ◽  
Mark Buehner ◽  
P. A. Vaillancourt ◽  
Cécilien Charette ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Medium Range ◽  
Summer Hemisphere ◽  
Forecasting System ◽  
Winter Hemisphere ◽  
Radiation Scheme

Abstract A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.

scholarly journals Probabilistic Forecasts Using Analogs in the Idealized Lorenz96 Setting

2011 ◽  
Vol 139 (6) ◽  
pp. 1960-1971 ◽  
Author(s):  
Jakob W. Messner ◽  
Georg J. Mayr
Keyword(s):  
Logistic Regression ◽  
Systematic Errors ◽  
Lead Times ◽  
Model Output ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Direct Model ◽  
Nwp Model

Abstract Three methods to make probabilistic weather forecasts by using analogs are presented and tested. The basic idea of these methods is that finding similar NWP model forecasts to the current one in an archive of past forecasts and taking the corresponding analyses as prediction should remove all systematic errors of the model. Furthermore, this statistical postprocessing can convert NWP forecasts to forecasts for point locations and easily turn deterministic forecasts into probabilistic ones. These methods are tested in the idealized Lorenz96 system and compared to a benchmark bracket formed by ensemble relative frequencies from direct model output and logistic regression. The analog methods excel at longer lead times.

scholarly journals Monthly Weather Forecasts in a Pest Forecasting Context: Downscaling, Recalibration, and Skill Improvement

2012 ◽  
Vol 51 (9) ◽  
pp. 1633-1638 ◽  
Author(s):  
Martin Hirschi ◽  
Christoph Spirig ◽  
Andreas P. Weigel ◽  
Pierluigi Calanca ◽  
Jörg Samietz ◽  
...  
Keyword(s):  
Insect Pest ◽  
Cydia Pomonella ◽  
Codling Moth ◽  
Lead Times ◽  
Practical Applications ◽  
Weather Forecasts ◽  
Weather Information ◽  
Agricultural Applications ◽  
Resampling Procedure ◽  
Skill Improvement

AbstractMonthly weather forecasts (MOFCs) were shown to have skill in extratropical continental regions for lead times up to 3 weeks, in particular for temperature and if weekly averaged. This skill could be exploited in practical applications for implementations exhibiting some degree of memory or inertia toward meteorological drivers, potentially even for longer lead times. Many agricultural applications fall into these categories because of the temperature-dependent development of biological organisms, allowing simulations that are based on temperature sums. Most such agricultural models require local weather information at daily or even hourly temporal resolution, however, preventing direct use of the spatially and temporally aggregated information of MOFCs, which may furthermore be subject to significant biases. By the example of forecasting the timing of life-phase occurrences of the codling moth (Cydia pomonella), which is a major insect pest in apple orchards worldwide, the authors investigate the application of downscaled weekly temperature anomalies of MOFCs for use in an impact model requiring hourly input. The downscaling and postprocessing included the use of a daily weather generator and a resampling procedure for creating hourly weather series and the application of a recalibration technique to correct for the original underconfidence of the forecast occurrences of codling moth life phases. Results show a clear skill improvement of up to 3 days in root-mean-square error over the full forecast range when incorporating MOFCs as compared with deterministic benchmark forecasts using climatological information for predicting the timing of codling moth life phases.

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