Climate variations and change evident in high-quality climate data for Australia's Antarctic and remote island weather stations

2013 ◽  
Vol 62 (4) ◽  
pp. 247-261 ◽  
Author(s):  
B Jovanovic ◽  
K Braganza ◽  
D Collins ◽  
D Jones
Keyword(s):  
Climate Variations ◽  
Climate Data ◽  
High Quality ◽  
Remote Island ◽  
Weather Stations

scholarly journals 1 km Monthly Precipitation and Temperatures Dataset for China from 1952 to 2019 based on a Brand-New and High-Quality Baseline Climatology Surface

2021 ◽  
Author(s):  
Haibo Gong ◽  
Xueqiao Xiang ◽  
Huiyu Liu ◽  
Xiaojuan Xu ◽  
Fusheng Jiao ◽  
...  
Keyword(s):  
High Performance ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Climate Data ◽  
High Quality ◽  
Temperature Estimation ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
Better Than

Abstract. Long-term climate data and high-quality baseline climatology surface with high resolution are highly essential to multiple fields in climatological, ecological, hydrological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_timeseries). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Meanwhile, climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_timeseries. Our results showed that ChinaClim_baseline exhibited very high performance. For precipitation estimation, the value of all R2 was over 0.860, and the values of RMSEs and MAEs were 8.149 mm~21.959 mm and 2.787~14.125 mm, respectively. Temperature elements had an average R2 of 0.967~0.992, an average MAEs of 0.321~0.785 °C, and an average RMSEs between 0.485 and 1.233 °C for all months. ChinaClim_baseline performed much better than WorldClim2 and CHELSA and there were many spatial discrepancies captured among those surfaces, especially in summer months and the regions with low-density weather stations in temperate continental and high cold Tibetan Plateau. For ChinaClim_timeseries, precipitation had an average R2 of 0.699~0.923, an average RMSE between 7.449 mm and 56.756 mm, and an average of MAE of 4.263~40.271 mm for all months. Temperature elements had an average R2 of 0.936~0.985, an average RMSE between 0.807 °C and 1.766 °C, and an average MAE of 0.548~1.236 °C for all months. Compared with Peng's climate surface and CHELSAcruts, R2 increased by approximately 6 %, RMSE and MAE decreased by approximately 15 % for precipitation; R2 of temperatures had no obviously changes, but RMSE and MAE decreased by 8.37~34.02 %. The results showed that the interannual variations of ChinaClim_timeseries performed much better than other datasets, thanks to the help of ChinaClim_baseline and satellite-driven products. However, ChinaClim_baseline did not significantly improve the accuracy of precipitation estimation, but it greatly improved the accuracy of temperature estimation; the satellite-driven TRMM3B43 anomaly greatly improve the accuracy of precipitation estimation after 1998, while the LST anomaly did not effectively improve the accuracy of temperature estimation. ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_timeseries of 1 km spatial resolution based on ChinaClim_baseline, is very suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China. Here, ChinaClim_baseline is available at https://doi.org/10.5281/zenodo.4287824 (Gong, 2020a), ChinaClim_timeseries of precipitation is available at https://doi.org/10.5281/zenodo.4288388 (Gong, 2020b), ChinaClim_timeseries of maximum temperature is available at https://doi.org/10.5281/zenodo.4288390 (Gong, 2020c) and ChinaClim_timeseries of minimum temperature is available at https://doi.org/10.5281/zenodo.4288392 (Gong, 2020d).

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Variabilidade Espacial da Erosividade das Chuvas para o Estado da Paraíba (Variability of Spatial Erosivity of Rainfall in the Paraíba State)

2014 ◽  
Vol 7 (4) ◽  
pp. 691
Author(s):  
Bernardo Starling Dorta do Amaral ◽  
João Filadelfo de Carvalho Neto ◽  
Richarde Marques da Silva ◽  
José Carlos Dantas
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Rainfall Characteristics ◽  
R Factor ◽  
Erosivity Index ◽  
The Mean ◽  
Weather Stations ◽  
Soil Loss Equation

As características específicas das chuvas variam entre regiões, e o conhecimento da sua potencialidade erosiva é necessário para o planejamento dos recursos hídricos. Este estudo determinou a erosividade, analisou a variabilidade espacial da precipitação e o coeficiente de chuva para o Estado da Paraíba mediante técnicas de Sistemas de Informação Geográfica. Para a realização deste estudo foram utilizados dados climatológicos de 98 estações climatológicas da Embrapa, com séries de 1911 a 1990. Em seguida as informações sobre a erosividade foram processadas cartograficamente. O valor médio anual da erosividade das chuvas com base no índice EI30 para o Estado da Paraíba foi de 5.032,03 MJ.mm/ha/h, valor que representa o Fator “R” da Equação Universal de Perdas de Solo (USLE). As equações de regressão entre erosividade e precipitação e coeficiente de chuva não foram significativas. As principais conclusões são que: (a) os índices de erosividade encontrados são maiores na zona litorânea do que nas demais porções do Estado, e (b) as erosividades encontradas variaram de acordo com os valores da precipitação.   A B S T R A C T Specific rainfall characteristics vary among regions and their erosion potential must be known for the planning of water resources. This study analyzed the erosivity and rainfall variability and precipitation coefficient for Paraíba State based on Geographic Information Systems techniques. In order In this paper 98 climatological stations of Embrapa were used, with rainfall data of 1911 to 1990. For this study we use d climate data from 98 weather stations of Embrapa, with series from 1911 to 1990. Additionally we processed the information of the erosivity index cartographically by year and microregions. The mean annual value of erosivity was 5,032.03 MJ.mm/ha/h, which is to be used as “R” Factor in the Universal Soil Loss Equation (USLE) for Paraíba State and surrounding regions with similar climatic conditions. The main conclusions are that: (a) erosivity indexes are higher in coastal areas than in inland areas, and (b) the erosivity range according to the precipitation.   Keywords: erosivity, rainfall, water resources   

scholarly journals Climatic water indices for viticulture

2018 ◽  
Vol 53 (6) ◽  
pp. 765-768
Author(s):  
Marco Antônio Fonseca Conceição ◽  
Jorge Tonietto ◽  
Reginaldo Teodoro de Souza
Keyword(s):  
Climate Data ◽  
Water Index ◽  
Historical Series ◽  
Weather Stations ◽  
Water Indices

Abstract: The objective of this work was to evaluate the performance of vineyard water indices in different grape-growing regions. The climate data used come from the historical series of weather stations located in 18 countries. The evaluated indices were the following: dryness, Zuluaga, humidity, aridity, moisture, and the grapevine water index. The grapevine water index and the indices of drought, moisture, and aridity exhibit similar performances, which makes them suitable to be used equivalently in climatological studies of grapevine regions.

scholarly journals Local knowledge based perceptions on climate change and its impacts in the Rakaposhi valley of Gilgit-Baltistan, Pakistan

2019 ◽  
Vol 12 (2) ◽  
pp. 222-237 ◽  
Author(s):  
Laxmi Dutt Bhatta ◽  
Erica Udas ◽  
Babar Khan ◽  
Anila Ajmal ◽  
Roheela Amir ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Local Knowledge ◽  
Plant Distribution ◽  
Precipitation Pattern ◽  
Climate Data ◽  
Local Perceptions ◽  
Content Type ◽  
Knowledge Based ◽  
Weather Stations

Purpose The purpose of this paper is to understand local perceptions on climate change and its impacts on biodiversity, rangeland, agriculture and human health. Design/methodology/approach A household survey with 300 interviewees and focus group discussions with key stakeholders were conducted and validated at two steps, using the climate data from the nearest weather stations and reviewing literatures, to correlate the local perceptions on climate change and its impacts. Findings Majority of the respondents reported an increase in temperature and change in the precipitation pattern with increased hazardous incidences such as floods, avalanches and landslides. Climate change directly impacted plant distribution, species composition, disease and pest infestation, forage availability, agricultural productivity and human health risks related to infectious vector-borne diseases. Research limitations/implications Because of the remoteness and difficult terrain, there are insufficient local weather stations in the mountains providing inadequate scientific data, thus requiring extrapolation from nearest stations for long-term climate data monitoring. Practical implications The research findings recommend taking immediate actions to develop local climate change adaptation strategies through a participatory approach that would enable local communities to strengthen their adaptive capacity and resilience. Social implications Local knowledge-based perceptions on climate change and its impacts on social, ecological and economic sectors could help scientists, practitioners and policymakers to understand the ground reality and respond accordingly through effective planning and implementing adaptive measures including policy formulation. Originality/value This research focuses on combining local knowledge-based perceptions and climate science to elaborate the impacts of climate change in a localised context in Rakaposhi Valley in Karakoram Mountains of Pakistan.

scholarly journals Climatic records and linkage along an altitudinal gradient in the southern slope of Nepal Himalaya

2017 ◽  
Vol 53 ◽  
pp. 47-56 ◽  
Author(s):  
Binod Dawadi
Keyword(s):  
Regression Models ◽  
Similar Rate ◽  
High Mountain ◽  
Climate Data ◽  
Southern Slope ◽  
Nepal Himalaya ◽  
Lower Elevation ◽  
Similar Range ◽  
Weather Stations ◽  
Highly Correlated

To validate the climatic linkages under different topographic conditions, observational climate data at four automated weather stations (AWS) in different elevations, ranging from 130 m asl. to 5050 m asl., on the southern slope of the Nepal Himalayas was examined. the variation of means and distribution of daily, 5-days, 10-days, and monthly average/sum of temperature/ precipitation between the stations in the different elevation was observed. Despite these differences, the temperatures records are consistent in different altitudes, and highly correlated to each other while the precipitation data shows comparatively weaker correlation. The slopes (0.79-1.18) with (R2 >0.64) in the regression models for high Mountain to high Himalaya except in November and 0.56-1.14 (R2 >0.50) for mid-hill and high Mountain except January, December, June indicate the similar rate of fluctuation of temperature between the stations in the respective region. These strong linkages and the similar range of fluctuation of temperature in the different elevation indicate the possibilities of their use of lower elevation temperature data to represent the higher elevation sites for paleoclimatic calibration. However, the associations of precipitation between the stations at the different elevation are not as strong as the temperature due to heterogeneous topographical features and steep altitudinal contrast.

scholarly journals The SASSCAL contribution to climate observation, climate data management and data rescue in Southern Africa

2015 ◽  
Vol 12 (1) ◽  
pp. 171-177 ◽  
Author(s):  
F. Kaspar ◽  
J. Helmschrot ◽  
A. Mhanda ◽  
M. Butale ◽  
W. de Clercq ◽  
...  
Keyword(s):  
Data Management ◽  
Southern Africa ◽  
National Level ◽  
Climate Data ◽  
Time Data ◽  
Observation Network ◽  
Management Concepts ◽  
Weather Stations ◽  
Building Activities ◽  
Science Service

Abstract. A major task of the newly established "Southern African Science Service Centre for Climate Change and Adaptive Land Management" (SASSCAL; www.sasscal.org) and its partners is to provide science-based environmental information and knowledge which includes the provision of consistent and reliable climate data for Southern Africa. Hence, SASSCAL, in close cooperation with the national weather authorities of Angola, Botswana, Germany and Zambia as well as partner institutions in Namibia and South Africa, supports the extension of the regional meteorological observation network and the improvement of the climate archives at national level. With the ongoing rehabilitation of existing weather stations and the new installation of fully automated weather stations (AWS), altogether 105 AWS currently provide a set of climate variables at 15, 30 and 60 min intervals respectively. These records are made available through the SASSCAL WeatherNet, an online platform providing near-real time data as well as various statistics and graphics, all in open access. This effort is complemented by the harmonization and improvement of climate data management concepts at the national weather authorities, capacity building activities and an extension of the data bases with historical climate data which are still available from different sources. These activities are performed through cooperation between regional and German institutions and will provide important information for climate service related activities.

High-quality spatial climate data-sets for Australia

2009 ◽  
Vol 58 (04) ◽  
pp. 233-248 ◽  
Author(s):  
D Jones ◽  
W Wang ◽  
R Fawcett
Keyword(s):  
Data Sets ◽  
Climate Data ◽  
High Quality

scholarly journals Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

2017 ◽  
Vol 38 (4Supl1) ◽  
pp. 2265
Author(s):  
Rodrigo Cornacini Ferreira ◽  
Rubson Natal Ribeiro Sibaldelli ◽  
Heverly Morais ◽  
Otávio Jorge Grigoli Abi Saab ◽  
José Renato Bouças Farias
Keyword(s):  
Spatial Variability ◽  
Meteorological Data ◽  
Rainfall Data ◽  
Data Series ◽  
Weather Station ◽  
Climate Data ◽  
Crop Models ◽  
Area Of Interest ◽  
Grain Productivity ◽  
Weather Stations

Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.

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