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- Author or Editor: Gerard van der Schrier x
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Abstract
Climate indices are analyzed using a newly developed dataset with station-based daily data for Southeast Asia. With rice the staple food of the diet in the region, the indices used are aimed at agriculture, specifically rice production, and include the onset of the wet season and the nighttime temperature. Three indices are used to estimate the onset of the wet season. Despite a quantitative lack of similarity between these indices (although they are strongly correlated), the progression of the wet season over the area matches existing descriptions. Trends in the onset date of the wet season calculated over 1971–2012 are only statistically significant for a few stations; there are no signs that a wide spread delay as anticipated by future climate scenarios is already taking place. A positive trend in the nighttime temperature over the region is observed with values up to 0.7°C decade−1. For a selection of stations the change in distribution of nighttime temperatures is analyzed when comparing the 1971–90 period with the 1991–2010 period. They show a shift of the median to higher temperatures, and the decline in the number of relatively cool nights is stronger than the increase in the number of relatively warm nights.
Abstract
Climate indices are analyzed using a newly developed dataset with station-based daily data for Southeast Asia. With rice the staple food of the diet in the region, the indices used are aimed at agriculture, specifically rice production, and include the onset of the wet season and the nighttime temperature. Three indices are used to estimate the onset of the wet season. Despite a quantitative lack of similarity between these indices (although they are strongly correlated), the progression of the wet season over the area matches existing descriptions. Trends in the onset date of the wet season calculated over 1971–2012 are only statistically significant for a few stations; there are no signs that a wide spread delay as anticipated by future climate scenarios is already taking place. A positive trend in the nighttime temperature over the region is observed with values up to 0.7°C decade−1. For a selection of stations the change in distribution of nighttime temperatures is analyzed when comparing the 1971–90 period with the 1991–2010 period. They show a shift of the median to higher temperatures, and the decline in the number of relatively cool nights is stronger than the increase in the number of relatively warm nights.
Abstract
In the Agile Way of Working (AoW), a group of developers jointly work to efficiently realize a project. Here we report on the application of AoW in meteorological research and development (R&D) outside of the software engineering environment. Three projects were formulated, derived from the observations strategy (2015) of the Royal Netherlands Meteorological Institute (KNMI). An initial phase of preparation consisted of breaking down the workload into tasks to be accomplished by individual project members and achievable in two one-week sprints. Sprints consisted of daily stand-ups, where accomplishments, work intentions, and obstacles were discussed, followed by project work in a joint working environment. The three projects identified were 1) flying a drone to detect boundary layer evolution, 2) monitoring the quality of the precipitation measurement system, and 3) realizing a platform for merging third-party data with meteorological observations. The preparation phase proved to be vitally important to each of the projects. The roles of the product owner and Scrum master in streamlining and guiding these projects were essential to the success of the sprint weeks, but the joint group settings worked well for only two of the three projects. While team members were positive about their experience with the AoW, the challenge remains to fuse the traditional individual work practice of researchers with that of software engineers, who are experienced in working in a group setting.
Abstract
In the Agile Way of Working (AoW), a group of developers jointly work to efficiently realize a project. Here we report on the application of AoW in meteorological research and development (R&D) outside of the software engineering environment. Three projects were formulated, derived from the observations strategy (2015) of the Royal Netherlands Meteorological Institute (KNMI). An initial phase of preparation consisted of breaking down the workload into tasks to be accomplished by individual project members and achievable in two one-week sprints. Sprints consisted of daily stand-ups, where accomplishments, work intentions, and obstacles were discussed, followed by project work in a joint working environment. The three projects identified were 1) flying a drone to detect boundary layer evolution, 2) monitoring the quality of the precipitation measurement system, and 3) realizing a platform for merging third-party data with meteorological observations. The preparation phase proved to be vitally important to each of the projects. The roles of the product owner and Scrum master in streamlining and guiding these projects were essential to the success of the sprint weeks, but the joint group settings worked well for only two of the three projects. While team members were positive about their experience with the AoW, the challenge remains to fuse the traditional individual work practice of researchers with that of software engineers, who are experienced in working in a group setting.
ABSTRACT
Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).
ABSTRACT
Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).
Abstract
This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.
Abstract
This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.
Abstract
The International Climate Assessment & Dataset (ICA&D) concept provides climate services on a regional scale for users in participating countries and the broader scientific community. It builds on the expertise gained in Europe, where national meteorological services collaborate by sharing climate data in order to produce regional climate assessments. Universities and data-rescue initiatives have joined this collaboration. The result is a web-based information system that combines quality-controlled daily station data with derived climate indices. Indices are provided for mean and extreme climate conditions including droughts, heat waves, and heavy rainfall events. ICA&D systems currently exist in Europe and in three regions of the world vulnerable to climate change: Southeast Asia, Latin America, and West Africa. Historical perspectives on climate variability and change are integrated with the monitoring of current climate evolution through regular updates of the data series obtained from meteorological observing stations. Web users have access to plots and maps of climate indices, showing time series, trends, or deviations from climatology. All information can be downloaded for noncommercial research and educational purposes, except for a part of the daily data that the data provider does not want to share.
Abstract
The International Climate Assessment & Dataset (ICA&D) concept provides climate services on a regional scale for users in participating countries and the broader scientific community. It builds on the expertise gained in Europe, where national meteorological services collaborate by sharing climate data in order to produce regional climate assessments. Universities and data-rescue initiatives have joined this collaboration. The result is a web-based information system that combines quality-controlled daily station data with derived climate indices. Indices are provided for mean and extreme climate conditions including droughts, heat waves, and heavy rainfall events. ICA&D systems currently exist in Europe and in three regions of the world vulnerable to climate change: Southeast Asia, Latin America, and West Africa. Historical perspectives on climate variability and change are integrated with the monitoring of current climate evolution through regular updates of the data series obtained from meteorological observing stations. Web users have access to plots and maps of climate indices, showing time series, trends, or deviations from climatology. All information can be downloaded for noncommercial research and educational purposes, except for a part of the daily data that the data provider does not want to share.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
