scholarly journals Verification and Bias Adjustment of ECMWF SEAS5 Seasonal Forecasts over Europe for Climate Service Applications

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 181
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Paola Marson ◽  
Christian Viel ◽  
Lucas Grigis
Keyword(s):  
Seasonal Forecasting ◽  
Climate Variables ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
Climate Services ◽  
Weather Regimes ◽  
Bias Adjustment ◽  
Medium Range ◽  
Climate Service ◽  
Forecasting System

This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

Impact of bias correction techniques on an ensemble of seasonal discharge forecast for the Danube upstream of Vienna

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Bias Correction ◽  
Hydrological Model ◽  
Systematic Errors ◽  
Seasonal Forecasts ◽  
Scaling Factors ◽  
Quantile Mapping ◽  
Forecasting System ◽  
Mapping Techniques ◽  
Seasonal Discharge

<p>The skill of seasonal hydro-meteorological forecasts with a lead time of up to six months is currently limited, since they frequently exhibit random but also systematic errors. Bias correction algorithms can be applied and provide an effective approach in removing historical biases relative to observations. Systematic errors in hydrology model outputs can be consequence of different sources: i) errors in meteorological data used as input data, ii) errors in the hydrological model response to climate forcings, iii) unknown/unobservable internal states and iv) errors in the model parameterizations, also due to unresolved subgrid scale variability.</p><p>Normally, bias correction techniques are used to correct meteorological, e.g. precipitation data, provided by climate models. Only few studies are available applying these techniques to hydrological model outputs. Standard bias correction techniques used in literature can be classified into scaling-, and distributional-based methods. The former consists of using multiplicative or additive scaling factors to correct the modeled simulations, while the later methods are quantile mapping techniques that fit the distribution of the simulation to fit to the observations. In this study, the impact of different bias correction techniques on the seasonal discharge forecasts skill is assessed.</p><p>As a case study, a seasonal discharge forecasting system developed for the Danube basin upstream of Vienna, is used. The studied basin covers an area of around 100 000 km<sup>2</sup> and is subdivided in 65 subbasins, 55 of them gauged with a long historical record of observed discharge. The forecast system uses the calibrated hydrological model, COSERO, which is fed with an ensemble of seasonal temperature and precipitation forecasts. The output of the model provides an ensemble of seasonal discharge forecasts for each of the (gauged) subbasins. Seasonal meteorological forecasts for the past (hindcast), together with historical discharge observations, allow to assess the quality of the seasonal discharge forecasting system, also including the effects of different bias correction methods. The corrections applied to the discharge simulations allow to eliminate potential systematic errors between the modeled and observed values.</p><p>Our findings generally suggest that the quality of the seasonal forecasts improve when applying bias correction. Compared to simpler methods, which use additive or multiplicative scaling factors, quantile mapping techniques tend to be more appropriate in removing errors in the ensemble seasonal forecasts.</p>

scholarly journals Upgrade of a climate service tailored to water reservoirs management 

2021 ◽  
Author(s):  
Eroteida Sánchez-García ◽  
Inmaculada Abia ◽  
Marta Domínguez ◽  
José Voces ◽  
Juan Carlos Sánchez-Perrino ◽  
...  
Keyword(s):  
Winter Season ◽  
Scientific Progress ◽  
Water Reservoir ◽  
Water Inflow ◽  
Hydrological Models ◽  
Seasonal Forecasts ◽  
Web Tool ◽  
Water Reservoirs ◽  
Climate Services ◽  
Climate Service

<p>In this paper we present the upgrade  of  a web tool designed to help in the decision making process for water reservoirs management in Spain. The tool, called S-ClimWaRe (Seasonal Climate predictions in support of Water Reservoirs management) is organized in two main displaying panels. The first one -diagnostic panel- allows the user to explore, for any water reservoir or grid point over continental Spain, the existing hydrological variability and risk linked to climate variability.  The second one -forecasting panel- provides probabilistic seasonal predictions for some variables of interest. Following users’ need the tool initially covers the extended winter season (from November to March), when the North Atlantic Oscillation pattern strongly influences the hydrological interannual variability in South-Western Europe. This climate service is fully user driven with a strong commitment of users and stakeholders that has allowed  continuous improvement of this tool, meeting users requirements and incorporating latest scientific progress.<br>The latest S-ClimWaRe version -developed in the framework of the MEDSCOPE project within the European Research Area for Climate Services (ERA4CS) initiative- includes some technical enhancements requested by customers and new seasonal predictions obtained through application of two post-processing steps to ECMWF System-5 forecasts. These two steps consist of a  downscaling statistical procedure and a new methodology that combines different skilful NAO forecasts to create an optimal NAO pdf that is then used to weight the ensemble members forecasts of hydrological variables. The new upgraded S-ClimWaRe web tool enriches the forecasting panel with precipitation and water inflow forecast skill, and provides additional forecasts for accumulated snowfall and temperature. A prototype based on two different hydrological models to produce the seasonal forecasts of water inflow has also been tested over a pilot dam. These hydrological models are driven by the  downscaled precipitation and temperature forecasts also introduced in the web viewer. The assessment of this downscaling procedure shows promising results with respect to the existing seasonal forecasts based on a statistical approach.</p>

Assessment of Météo-France current seasonal forecasting system S7 and outlook on the upcoming S8

2021 ◽  
Author(s):  
Jean-François Guérémy ◽  
Clotilde Dubois ◽  
Christian Viel ◽  
Laurent Dorel ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Model ◽  
Coupled System ◽  
Seasonal Forecasting ◽  
Coupled Climate Model ◽  
Seasonal Forecasts ◽  
Forecasting System ◽  
Initialization Strategy ◽  
The Eu

<p>In the framework of the EU <span>C</span><span>opernicus</span> <span>Climate Change Service </span><span>(</span>C3S) program, a new coupled system has been developed at Météo-France (MF) to carry out seasonal forecasts at a 7-month range. This system (called S7) is in operation in real time since October 2019. S7 is based upon the MF coupled climate model CNRM-CM6 used for CMIP6 simulations, in its high resolution configuration: ARPEGE-Climat (Tl359-0.5° l91, including different tuning choices for the physics), NEMO 3.6 (0.25° l75) and the OASIS coupler. The aim of this presentation is twofold.</p><p>First, an assessment of S7 performance will be presented in terms of biases, and both deterministic and probabilistic predictability scores. A comparison with the earlier MF system and the current ECMWF system will be shown.</p><p>Second, incremental updates from S7 to S8, to be in operation in June 2021, will be presented and assessed versus S7. The upgrade includes a larger atmospheric resolution from l91 to l137, together with a coupled initialization strategy to replace the earlier independent atmospheric and oceanic initialization.</p>

Does accounting for the direct-radiative effect of prognostic aerosols improve 5-day temperature forecast of the ECMWF weather forecast model ?

2020 ◽  
Author(s):  
Johannes Flemming ◽  
Alessio Bozzo ◽  
Jerome Barre ◽  
Richard Engelen ◽  
Sebastien Garrigues ◽  
...  
Keyword(s):  
Temperature Response ◽  
Weather Forecast ◽  
Forecast Model ◽  
Atmospheric Composition ◽  
Forecast Errors ◽  
Test Bed ◽  
Seasonal Forecasts ◽  
Temperature Forecast ◽  
Medium Range ◽  
Forecasting System

<p>The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts.</p><p>We will discuss the changes and improvements of temperature forecast errors (i) using typical NWP scores and (ii) by applying an event based approach that focuses on episodes of high aerosol burdens such as the transport of Sahara dust to Europe during the heatwave in June 2019. In more detail we will show to what extent the realism of the prognostic aerosol fields influences the temperature response by considering aerosol forecast which were, or were not, improved by data assimilation of aerosol optical depth at the start of the forecast. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a making progress.</p>

An Ensemble Generation Method for Seasonal Forecasting with an Ocean–Atmosphere Coupled Model

2005 ◽  
Vol 133 (2) ◽  
pp. 441-453 ◽  
Author(s):  
Jérôme Vialard ◽  
Frédéric Vitart ◽  
Magdalena A. Balmaseda ◽  
Timothy N. Stockdale ◽  
David L. T. Anderson
Keyword(s):  
Initial Conditions ◽  
Forecast Error ◽  
Random Perturbation ◽  
Coupled Model ◽  
Model Error ◽  
Ensemble Forecasting ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Forecasting System ◽  
Ensemble Generation

Abstract Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.

Assessing consistency across climate datasets for the potential detectability of extreme events in seasonal forecasting using agroclimatic indicators

2020 ◽  
Author(s):  
Jose Maria Costa Saura ◽  
Valentina Bacciu ◽  
Valentina Mereu ◽  
Antonio Trabucco ◽  
Donatella Spano
Keyword(s):  
Probability Distribution ◽  
Spatial Patterns ◽  
Water Availability ◽  
Fire Risk ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Medium Range ◽  
Validation Procedure ◽  
Agroclimatic Indicators ◽  
Definition Of

<p>Seasonal forecasts are medium-range climate predictions that, used for calculating agroclimatic indicators, might potentially help land managers for best decision making. To assess their reliability seasonal forecasts are commonly contrasted against observed datasets, e.g. gridded data coming from reanalysis, classifying yearly pixel conditions in into/out of the norm events (i.e. using the 33<sup>th</sup> and 66<sup>th</sup> percentiles along a time series to define the occurrence of out of the norm events). Potential differences in the shape of the probability distribution across observed climate datasets might influence the results in the validation procedure of seasonal forecasting since the definition of out of the norm events depends on the properties of the statistical distribution. Here, we assess for different agroclimatic indicators related with water availability, vegetation thermal needs and fire risk, the spatial patterns of skewness using a range of climate datasets, i.e. ERA5, E-OBS and WFDEI along a 30 year period. Skewness represents the degree of asymmetry of the probability distribution evidencing locations in which out of the norm events highly differ from mean conditions which might suggest a potentially higher detectability. Common spatial patterns of great skewness (either positive or negative) across observed dataset might suggest areas with high and consistent detectability whereas contrasting patterns might suggest higher uncertainty for the validation procedure.</p>

scholarly journals Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130290 ◽  
Author(s):  
Antje Weisheimer ◽  
Susanna Corti ◽  
Tim Palmer ◽  
Frederic Vitart
Keyword(s):  
Time Scales ◽  
Weather Prediction ◽  
Seasonal Forecasting ◽  
Near Surface ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact ◽  
Stochastically Perturbed

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region.

scholarly journals Forecast skill of the Indian monsoon and its onset in the ECMWF seasonal forecasting system 5 (SEAS5)

2021 ◽  
Author(s):  
Amulya Chevuturi ◽  
Andrew G. Turner ◽  
Stephanie Johnson ◽  
Antje Weisheimer ◽  
Jonathan K. P. Shonk ◽  
...  
Keyword(s):  
Large Scale ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Global Precipitation Climatology Project ◽  
Seasonal Forecasting ◽  
Weather Forecasts ◽  
Medium Range ◽  
Forecasting System ◽  
Dynamical Features ◽  
Global Precipitation

AbstractAccurate forecasting of variations in Indian monsoon precipitation and progression on seasonal time scales remains a challenge for prediction centres. We examine prediction skill for the seasonal-mean Indian summer monsoon and its onset in the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system 5 (SEAS5). We analyse summer hindcasts initialised on 1st of May, with 51 ensemble members, for the 36-year period of 1981–2016. We evaluate the hindcasts against the Global Precipitation Climatology Project (GPCP) precipitation observations and the ECMWF reanalysis 5 (ERA5). The model has significant skill at forecasting dynamical features of the large-scale monsoon and local-scale monsoon onset tercile category one month in advance. SEAS5 shows higher skill for monsoon features calculated using large-scale indices compared to those at smaller scales. Our results also highlight possible model deficiencies in forecasting the all India monsoon rainfall.

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