Statistical modelling

Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using antecedent sea surface temperatures primarily within the tropical oceans. Predictions are made on the assumption that historically observed relationships are expected to apply in the future. There are many conditions for such an assumption to be valid, including the need for high-quality datasets to ensure that the historical relationships are robustly measured, and the need for relationships to have a sound theoretical basis. Because of the possibility of identifying spurious relationships between the predictors and the predictands, the statistical model should be tested carefully on independent data. Most statistical models are based on linear regression, which provides a “best guess” forecast under the assumption that a given change in the value of a predictor results in a constant change in the expected value of the predictand regardless of the value of the predictor. Modifications to the linear model can be made or alternative statistical procedures used when there is good reason to expect a relationship to be nonlinear. However, other weaknesses of linear regression may also require these alternatives to be considered seriously. The primary problems with linear regression are multiplicity, multicolinearity, and non-normality of the predictands. Multiplicity refers to the effects of having a large number of candidate predictors: the danger of finding a spurious relationship increases. Multicolinearity arises when more than one predictor is used in the model and there are strong relationships between the predictors which can result in large errors in calculating the parameters of the model. Finally, a linear regression model may not be adequately constructed if the data being predicted have a strongly skewed or otherwise non-Gaussian distribution; seasonally accumulated precipitation often exhibits such problems. Alternative forms of linear and non-linear statistical models can be applied to address such distributional problems

CLIMATE CHANGE AND THE ROLE OF THE WORLD METEOROLOGICAL ORGANISATION

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منهجية مقترحة لتحسين دقة نموذج الارتفاع الرقمي العالمي المجاني SRTM-1 في مناطق من الساحل السوري

في هذه الدراسة تم تقييم الدقة الشاقولية لنموذج الارتفاع الرقمي المجاني SRTM-1 (منطقة الدراسة: دمسرخو – اللاذقية، البطحانية - طرطوس) من خلال مقارنته مع نقاط ارتفاعية مرجعية بالاعتماد على المعاملات إحصائية. استخدمنا منهجية تساعد في تحسين دقة النموذج SRTM-1، حيث اعتمدنا حقن نقاط ارتفاعية حقلية ضمن هذا النموذج. تم زيادة عدد النقاط المدمجة بمعدل 10 نقاط في كل مرة حتى وصلنا إلى 220 نقطة، وأجريت مقارنة مع النقاط الارتفاعية المرجعية في كل عملية للدمج. في المنطقة السهلية (دمسرخو) تبين وجود تحسن في دقة SRTM-1 بنسبة 22.5 % حيث انخفض الانحراف المعياري من1.86 m إلى m 1.44، وكان هناك ارتباط عكسي جيد بين عدد النقاط المدخلة والانحراف المعياري، مما يثبت أنها طريقة عملية وبسيطة لزيادة دقة SRTM-1 إلى حد معين، غير أن زيادة عدد النقاط عن حد معين (في دراستنا وصلنا الى 250 نقطة) لم نلحظ اي تحسن بالدقة. كذلك بالنسبة للمنطقة ذات التلال (البطحانية) فقد قمنا بحقن 10 نقاط كل مرة ضمن النموذج الارتفاعي حتى وصلنا إلى 340 نقطة، تحسنت الدقة بنسبة % 47.6 حيث انخفض الانحراف المعياري منm  8.08 إلى4.23 m  

Lief_etal_ESIP2018summer_WMO-SMM-CD.pdf

Introducing a WMO-wide Stewardship Maturity Matrix for Climate Data (SMM-CD

Stewardship maturity assessment tools for modernization of climate data management

High quality and well-managed climate data are the cornerstone of all climate services. Consistently assessing how well the data are managed is one way to establish or demonstrate the trustworthiness of the data. This paper presents the World Meteorological Organization’s (WMO) Stewardship Maturity Matrix for Climate Data (SMM-CD) and the subsidiary SMM-CD for National and Regional Purposes (SMM-CD_NRP). Both these matrices have been developed with the support of the WMO and its High-Quality Global Data Management Framework for Climate (HQ-GDMFC). These self-assessment tools enable data managers to discover WMO recommended data stewardship practices, determine a roadmap for future development and improvement, as well as compare their process against other data providers. Datasets which have been maturity assessed are included in the WMO Climate Data Catalogue, where users can include the results of these maturity assessments into their decision-making process. The SMM-CD contains four categories (data access, usability and usage, quality management, and data management) each of which has a number of aspects, with scores assigned to one of five levels. A smaller number of categories in the SMM-CD_NRP are assigned to four levels appropriate for operationally produced datasets which are national or regional in scope. We explore a number of case studies where these matrices have been applied, as well as supply links to where the Guidance Documents and Assessment Templates (which may be updated) can be found.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. Ge Peng was supported by NOAA National Centers for Environmental Information (NCEI) through the Cooperative Institute for Satellite Earth System Studies (CISESS) under Cooperative Agreement NA19NES4320002. Markus Donat was supported by the Spanish Ministry for the Economy, Industry and Competitiveness, grant reference RYC-2017-22964. KNMI hosted the EIG-CDM workshop and the development and maintenance of the WMO Catalogue for Climate Data.Peer ReviewedPostprint (published version

The Global Climate in 2015-2019

The Global Climate in 2015–2019 is part of the WMO Statements on Climate providing authoritative information on the state of the climate and impacts. It builds on operational monitoring systems at global, regional and national scales