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 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.

Criteria for Accrediting Expert Wine Judges

2013 ◽  
Vol 9 (1) ◽  
pp. 62-74 ◽  
Author(s):  
Robert Hodgson ◽  
Jing Cao
Keyword(s):  
Probability Distribution ◽  
Wine Industry ◽  
Ordinal Scale ◽  
Professional Experience ◽  
Wine Quality ◽  
Basic Premise ◽  
Definition Of ◽  
Jel Classifications

AbstractA test for evaluating wine judge performance is developed. The test is based on the premise that an expert wine judge will award similar scores to an identical wine. The definition of “similar” is parameterized to include varying numbers of adjacent awards on an ordinal scale, from No Award to Gold. For each index of similarity, a probability distribution is developed to determine the likelihood that a judge might pass the test by chance alone. When the test is applied to the results from a major wine competition, few judges pass the test. Of greater interest is that many judges who fail the test have vast professional experience in the wine industry. This leads to us to question the basic premise that experts are able to provide consistent evaluations in wine competitions and, hence, that wine competitions do not provide reliable recommendations of wine quality. (JEL Classifications: C02, C12, D81)

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 Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield

2005 ◽  
Vol 18 (10) ◽  
pp. 1566-1574 ◽  
Author(s):  
A. B. Potgieter ◽  
G. L. Hammer ◽  
H. Meinke ◽  
R. C. Stone ◽  
L. Goddard
Keyword(s):  
Spatial Patterns ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wheat Yield ◽  
Severe Drought ◽  
Seasonal Forecasts ◽  
El Niño Events ◽  
Australian Wheat ◽  
El Nino Events

Abstract The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.

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.

Snow as a source of predictability in seasonal forecasts?

2021 ◽  
Author(s):  
Danny Risto ◽  
Bodo Ahrens ◽  
Kristina Fröhlich
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Reanalysis Data ◽  
Heat Fluxes ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Predictive Skill ◽  
Temperature Forecast ◽  
Snow Water

<p>Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.</p>

scholarly journals Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

2016 ◽  
Vol 9 (6) ◽  
pp. 2055-2076 ◽  
Author(s):  
Lauriane Batté ◽  
Michel Déqué
Keyword(s):  
Stochastic Dynamics ◽  
Stochastic Perturbation ◽  
Coupled Model ◽  
Seasonal Forecasting ◽  
Stochastic Methods ◽  
Boreal Winter ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Error Statistics ◽  
The Impact

Abstract. Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode.

scholarly journals The physiology of drought stress in grapevine: towards an integrative definition of drought tolerance

2020 ◽  
Vol 71 (16) ◽  
pp. 4658-4676 ◽  
Author(s):  
Gregory A Gambetta ◽  
Jose Carlos Herrera ◽  
Silvina Dayer ◽  
Quishuo Feng ◽  
Uri Hochberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Drought Stress ◽  
Drought Tolerance ◽  
Water Availability ◽  
Economic Importance ◽  
Fruit Crop ◽  
Crop Varieties ◽  
Drought Tolerant ◽  
Definition Of ◽  
The Impact

Abstract Water availability is arguably the most important environmental factor limiting crop growth and productivity. Erratic precipitation patterns and increased temperatures resulting from climate change will likely make drought events more frequent in many regions, increasing the demand on freshwater resources and creating major challenges for agriculture. Addressing these challenges through increased irrigation is not always a sustainable solution so there is a growing need to identify and/or breed drought-tolerant crop varieties in order to maintain sustainability in the context of climate change. Grapevine (Vitis vinifera), a major fruit crop of economic importance, has emerged as a model perennial fruit crop for the study of drought tolerance. This review synthesizes the most recent results on grapevine drought responses, the impact of water deficit on fruit yield and composition, and the identification of drought-tolerant varieties. Given the existing gaps in our knowledge of the mechanisms underlying grapevine drought responses, we aim to answer the following question: how can we move towards a more integrative definition of grapevine drought tolerance?

Le Modele De Rasch

1988 ◽  
Vol 31 ◽  
pp. 57-70
Author(s):  
John H.A.L. de Jong
Keyword(s):  
Rasch Model ◽  
Reliability And Validity ◽  
Test Reliability ◽  
Stochastic Independence ◽  
Validation Procedure ◽  
Local Stochastic Independence ◽  
The One ◽  
Definition Of ◽  
Latent Traits ◽  
The Rasch Model

This paper provides an elementary introduction to the one parameter psychometric model known as the Rasch model. It explains the basic principles underlying the model and the concepts of unidimensionality, local stochastic independence, and additivity in non-mathematical terms. The requirements of measurement procedures, the measurement of latent traits, the control on model fit, and the definition of a trait are discussed. It is argued that the Rasch model is particularly appropriate to understand the mutual dependence of test reliability and validity. Examples from foreign language listening comprehension tests are used to illustrate the application of the model to a test validation procedure.

The definition of a multi-dimensional generalization of shot noise

1971 ◽  
Vol 8 (01) ◽  
pp. 128-135 ◽  
Author(s):  
D. J. Daley
Keyword(s):  
Stochastic Process ◽  
Probability Distribution ◽  
Absolute Convergence ◽  
List Type ◽  
Type Number ◽  
Homogeneous Poisson Process ◽  
Cauchy Principal ◽  
Principal Value ◽  
Definition Of ◽  
Image Position

The paper studies the formally defined stochastic process where {tj } is a homogeneous Poisson process in Euclidean n-space En and the a.e. finite Em -valued function f(·) satisfies |f(t)| = g(t) (all |t | = t), g(t) ↓ 0 for all sufficiently large t → ∞, and with either m = 1, or m = n and f(t)/g(t) =t/t. The convergence of the sum at (*) is shown to depend on (i) (ii) (iii) . Specifically, finiteness of (i) for sufficiently large X implies absolute convergence of (*) almost surely (a.s.); finiteness of (ii) and (iii) implies a.s. convergence of the Cauchy principal value of (*) with the limit of this principal value having a probability distribution independent of t when the limit in (iii) is zero; the finiteness of (ii) alone suffices for the existence of this limiting principal value at t = 0.

Sign in / Sign up

Export Citation Format

Share Document