Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe

2021 ◽  
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Lucas Grigis ◽  
Paola Marson ◽  
Jean-Michel Soubeyroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Management ◽  
Spatial Scales ◽  
Research Field ◽  
Skill Assessment ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Local Climate ◽  
Climate Conditions

<p>Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.</p><p>For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.</p><p>The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.</p><p>The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

The influence of aggregation and statistical post-processing on the sub-seasonal predictability of European temperatures

2020 ◽  
Author(s):  
Chiem van Straaten ◽  
Kirien Whan ◽  
Dim Coumou ◽  
Bart van den Hurk ◽  
Maurice Schmeits
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Forecast Skill ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Data Set ◽  
Weather Patterns ◽  
Rolling Window ◽  
Large Scale Flow ◽  
Obs Data

<p>The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large scale flow. Some aggregated statistic however, like the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2-meter temperature forecasts from the ECMWF sub-seasonal forecast system evolves with varying scales and thresholds. We apply temporal aggregation by rolling window averaging and spatial aggregation by hierarchical clustering. We verify 20 years of re-forecasts against the E-OBS data set and find that European predictability extends at maximum up to week 4. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skillful days on average, respectively.<br>The intuitive notion that higher levels of aggregation capture the larger scale and lower frequency variability and therefore tap into an extended predictability, holds in many cases. However, we show that the effect can saturate and that regional optimums exist, beyond which extra aggregation reduces the forecast skill horizon. We expect that such windows of predictability result from specific physical mechanisms that only modulate and extend predictability locally. To optimize sub-seasonal forecasts for Europe, aggregation should in certain cases thus be limited.</p>

scholarly journals Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Seasonal Prediction ◽  
General Circulation Models ◽  
Meridional Wind ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Continental Scale ◽  
Predictive Skill ◽  
Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

Plan for the development of specialized climate forecasting in NEACC

2021 ◽  
pp. 101-111
Author(s):  
S.V. Emelina ◽  
◽  
V.M. Khan ◽  
Keyword(s):  
Large Scale ◽  
Regional Climate ◽  
Optimal Strategies ◽  
Economic Sectors ◽  
Climate Services ◽  
Climate Conditions ◽  
Climate Risks ◽  
The North ◽  
Weather And Climate ◽  
North Eurasia

The possibility of developing specialized seasonal forecasting within the framework of the North Eurasia Climate Centre is discussed. The purpose of these forecasts is to access the impacts of significant large-scale anomalies of meteorological elements on various economic sectors for the timely informing of government services and private businesses to select optimal strategies for planning preventive measures. A brief overview of the groups of climatic risks in the context of the impacts on the socio-economic sphere is given according to the Russian and foreign bibliographic sources. Examples of the activities of some Regional Climate Centers that produce forecast information with an assessment of possible impacts of weather and climate conditions at seasonal scales on various human activities are given. Keywords: climate services, regional climate forums, weather and climate risks, North Eurasia Climate Centre

scholarly journals Applications of Models and Tools for Mesoscale and Microscale Thermal Analysis in Mid-Latitude Climate Regions—A Review

2021 ◽  
Vol 13 (22) ◽  
pp. 12385
Author(s):  
Gabriele Lobaccaro ◽  
Koen De Ridder ◽  
Juan Angel Acero ◽  
Hans Hooyberghs ◽  
Dirk Lauwaet ◽  
...  
Keyword(s):  
High Temperatures ◽  
Numerical Models ◽  
Spatial Scales ◽  
Built Environments ◽  
Local Climate ◽  
Climate Conditions ◽  
The Social ◽  
Climatic Environment ◽  
Intense Solar Radiation ◽  
Mobile Measurements

Urban analysis at different spatial scales (micro- and mesoscale) of local climate conditions is required to test typical artificial urban boundaries and related climate hazards such as high temperatures in built environments. The multitude of finishing materials and sheltering objects within built environments produce distinct patterns of different climate conditions, particularly during the daytime. The combination of high temperatures and intense solar radiation strongly perturb the environment by increasing the thermal heat stress at the pedestrian level. Therefore, it is becoming common practice to use numerical models and tools that enable multiple design and planning alternatives to be quantitatively and qualitatively tested to inform urban planners and decision-makers. These models and tools can be used to compare the relationships between the micro-climatic environment, the subjective thermal assessment, and the social behaviour, which can reveal the attractiveness and effectiveness of new urban spaces and lead to more sustainable and liveable public spaces. This review article presents the applications of selected environmental numerical models and tools to predict human thermal stress at the mesoscale (e.g., satellite thermal images and UrbClim) and the microscale (e.g., mobile measurements, ENVI-met, and UrbClim HR) focusing on case study cities in mid-latitude climate regions framed in two European research projects.

Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology 

2021 ◽  
Author(s):  
Wei Yang ◽  
Kean Foster ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Large Scale ◽  
Fuzzy Rule ◽  
River System ◽  
Reanalysis Data ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Weather Regimes ◽  
Hydrological Forecasting ◽  
Teleconnection Indices

<p>The hydrological forecasting on seasonal (up to 7 months ahead) timescales is needed for decision-making in the hydropower sector. Being one of the vital influencing factors on hydro-production, a lot of development in dynamical forecasting at seasonal timescales has been done recently. However, the forecast bias still remains in different variables and consequently the skill of corresponding streamflow forecasts varies from month to month.</p><p>This study aims to explore the potential for “pattern-based” seasonal hydrological forecasts that make use of hydrological weather regimes and teleconnection indices to improve forecast skill. The work is built on the hypothesis that hydrological weather regimes and teleconnection indices can be used to select analogue years (setting an ensemble) from a record of historical precipitation and temperature data with which to force a hydrological model to generate tailored seasonal forecasts of reservoir inflows. The hydrological weather regimes have been classified based on the concept of fuzzy sets using the anomalies of daily mean sea level pressure from reanalysis data (i.e., ERA-Interim). Precipitation records, measured in the Umeälven river basin during 1981-2016 are used as local observations to optimize each fuzzy rule that describes a type of “average” variability of local climate in terms of the frequency and magnitude of precipitation events. The teleconnection indices are compiled from the Climate Prediction Center, which describe global atmospheric variability. The methodology has been applied to 84 sub-catchments across seven of the most important hydropower producing river systems in Northern Sweden. However, the performance for the Umeälven river system is of particular interest here.</p><p>Comparing to the traditional Ensemble Streamflow Prediction (ESP) method, the “pattern-based” seasonal hydrological forecasting shows a marked improvement, which is likely due to the weighted analogue-ESP approach as well as the selected analogues using the large-scale climate information described by hydrological weather regimes and teleconnection indices. The general performance of the two different approaches for selecting the analogues are similar; however, occasionally there are large differences in both the best analysis lead times and the spread of skill across the sub-catchments suggesting that those results are achieved using analogues based on different physical processes.</p>

Maize Drought Hazard in the Northeast Farming Region of China: Unprecedented Events in the Current Climate

2019 ◽  
Vol 58 (10) ◽  
pp. 2247-2258 ◽  
Author(s):  
Chris Kent ◽  
Edward Pope ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Zhan Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Agricultural Sector ◽  
Climate Model ◽  
Height Anomaly ◽  
Climate Services ◽  
Climate Conditions ◽  
Drought Hazard ◽  
Climate Exposure ◽  
Near Term ◽  
Climate Model Simulations

AbstractThe Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.

Downscaling and bias correction of seasonal forecasts to support climate services for the Alpine regions

2020 ◽  
Author(s):  
Alice Crespi ◽  
Mattia Callegari ◽  
Felix Greifeneder ◽  
Claudia Notarnicola ◽  
Marcello Petitta ◽  
...  
Keyword(s):  
Energy Production ◽  
Production Management ◽  
Seasonal Forecast ◽  
Accurate Information ◽  
Sources Of Information ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Mapping Procedure ◽  
Alpine Regions ◽  
Wide Range

<p>The interest in trustable and accurate information about climate and its variability at local scale is currently increasing not only within the scientific community, but also by local stakeholders, political administrators and private companies. Clear, operative and close to the users’ needs climate information represent relevant support tools for a wide range of decision-making policies, including vulnerability assessment, risk management and energy production.</p><p>Seasonal forecasts, in particular, allow to provide predictions of the climate up to several months ahead and therefore they could represent precious sources of information for a wide range of activities, such as for the optimization of renewable energy sector. However, specific approaches are needed to deal with the probabilistic nature of seasonal forecasts and post-processing methods are required to adapt their large spatial resolution to the local scales of specific applications. This is particularly true for orographically complex areas, such as the Alpine regions, where coarse-resolution data could lead to remarkable under or overestimations in the predicted variables.</p><p>In this framework, we present a downscaled and bias-corrected version of seasonal forecasts provided by the ECMWF’s seasonal forecasting system (SEAS5) for temperature, precipitation and wind speed over the Alpine area and spanning the period 1983 – 2018. The approach is based on the bilinear interpolation of the 1°x1° original fields onto the target 0.25°x0.25° resolution and on the quantile-mapping procedure using ERA-5 reanalysis data for the calibration. The ERA-5 reanalysis dataset is chosen as reference in order to allow the application of the implemented scheme over different areas. The accuracy and skills of the post-processed seasonal forecast fields are evaluated, also in comparison with observations and the performance of alternative downscaling schemes.</p><p>The presented study supports the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

scholarly journals Climate Explorer: Improved Access to Local Climate Projections

2020 ◽  
Vol 101 (3) ◽  
pp. E265-E273
Author(s):  
Fredric Lipschultz ◽  
David D. Herring ◽  
Andrea J. Ray ◽  
Jay R. Alder ◽  
LuAnn Dahlman ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Easy Access ◽  
Climate Projections ◽  
Climate Data ◽  
Local Climate ◽  
Climate Conditions ◽  
Spatial And Temporal Scales ◽  
Climate Resilience ◽  
Projected Changes ◽  
The U.S

Abstract The goal of the U.S. Climate Resilience Toolkit’s (CRT) Climate Explorer (CE) is to provide information at appropriate spatial and temporal scales to help practitioners gain insights into the risks posed by climate change. Ultimately, these insights can lead to groups of local stakeholders taking action to build their resilience to a changing climate. Using CE, decision-makers can visualize decade-by-decade changes in climate conditions in their county and the magnitude of changes projected for the end of this century under two plausible emissions pathways. They can also check how projected changes relate to user-defined thresholds that represent points at which valued assets may become stressed, damaged, or destroyed. By providing easy access to authoritative information in an elegant interface, the Climate Explorer can help communities recognize—and prepare to avoid or respond to—emerging climate hazards. Another important step in the evolution of CE builds on the purposeful alignment of the CRT with the U.S. Global Change Research Program’s (USGCRP) National Climate Assessment (NCA). By closely linking these two authoritative resources, we envision that users can easily transition from static maps and graphs within NCA reports to dynamic, interactive versions of the same data within CE and other resources within the CRT, which they can explore at higher spatial scales or customize for their own purposes. The provision of consistent climate data and information—a result of collaboration among USGCRP’s federal agencies—will assist decision-making by other governmental entities, nongovernmental organizations, businesses, and individuals.

Going with the trend: forecasting seasonal climate conditions under climate change

2021 ◽  
Author(s):  
Yawen Shao ◽  
Quan J. Wang ◽  
Andrew Schepen ◽  
Dongryeol Ryu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Conditions ◽  
Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

Analysis of Mean Climate Conditions in Senegal (1971–98)

2006 ◽  
Vol 10 (5) ◽  
pp. 1-40 ◽  
Author(s):  
Souleymane Fall ◽  
Dev Niyogi ◽  
Fredrick H. M. Semazzi
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Regional Climate ◽  
Linear Regression Analysis ◽  
Regional Scale ◽  
Regional Climate Change ◽  
Warming Trend ◽  
Positive Trend ◽  
Local Climate ◽  
Climate Conditions

Abstract This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant correlation is found between the temperature index for Senegal and the Pacific sea surface temperatures during the January–April period, especially in the El Niño zone. In contrast to earlier regional-scale studies, precipitation does not show a negative trend and has remained largely unchanged, with a few locations showing a positive trend, particularly in the northeastern and southwestern regions. This study reveals a need for more localized climate analyses of the West Africa region because local climate variations are not always captured by large-scale analysis, and such variations can alter conclusions related to regional climate change.

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