Climate Research Unit cleared

Physics Today ◽  
2010 ◽  
Keyword(s):  
Research Unit ◽  
Climate Research Unit ◽  
Climate Research

Validation of Climate Research Unit High Resolution Time-Series Rainfall Data over Three Source Region: Results of 52 Years

2013 ◽  
Vol 726-731 ◽  
pp. 3542-3546 ◽  
Author(s):  
Jonathan Arthur Quaye-Ballard ◽  
Ru An ◽  
Richard Ruan ◽  
Kwaku Amaning Adjei ◽  
Samuel Akorful-Andam
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Source Region ◽  
Research Unit ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Climate Research Unit ◽  
Resolution Time ◽  
Map Algebra ◽  
Climate Research

The purpose of this paper was to validate the rainfall data of Climate Research Unit high resolution Time-Series version 3.1 (CRU TS 3.1) with meteorological ground-based Rain Gauge (RG) measurements and determine the possibility of its integration with ground-measured rainfall. The research primarily advocates on the need for complementing ground-based datasets with CRU TS 3.1global datasets for sustainable studies in protecting the environment. The Source Region of the Yellow, Yangtse and Lancang Rivers (SRYYLR), China was taken as the study area. The data was validated by using the data from seventeen meteorological RG stations at SRYYLR. Statistical technique based on Linear Regression (LR), Cumulative Residual Series Analysis (CRSA) and Geo-Spatial techniques based on batch processing, cell statistics, map algebra, re-sampling, extraction by mask, geo-statistical interpolation and profiling along transects by interpolation of a line were used. The study revealed that although CRU TS 3.1 datasets are underestimated compared to the RG datasets, they can be efficiently and effectively be used for rainfall trend analysis with 90% level of confidence because of the analyses by different techniques revealed similar profile trends.

scholarly journals Note de recherche : Grilles mensuelles de pluie en Afrique de l’Ouest et Centrale

2010 ◽  
Vol 23 (4) ◽  
pp. 325-333 ◽  
Author(s):  
Jean-Emmanuel Paturel ◽  
Ibrahim Boubacar ◽  
Agnès L’aour-Cres ◽  
Gil Mahe
Keyword(s):  
Nous Avons ◽  
Research Unit ◽  
Climate Research Unit ◽  
Grand Nombre ◽  
Afrique De L’Ouest ◽  
East Anglia ◽  
Climate Research ◽  
Base De Données ◽  
Afrique De L'ouest

L’ORSTOM, aujourd’hui IRD (Institut de Recherche pour le Développement), est un institut français de recherche qui est implanté en Afrique depuis près de 60 ans. Depuis lors, les agroclimatologues et les hydrologues de l’institut collectent, critiquent et complètent des données hydroclimatiques sur l’Afrique de l’Ouest et Centrale. Ayant pour objectif de mettre en place des outils permettant une modélisation hydrologique de cette partie du monde, notre équipe, HydroSciences Montpellier (HSM), a élaboré une base de données hydroclimatiques et environnementales (SIEREM). À partir de toutes les données pluviométriques collectées, nous avons élaboré des grilles mensuelles de pluie au ½ degré carré, ce qui correspond aux latitudes de la zone concernée à une superficie de l’ordre de 2 750 km². Afin d’évaluer ces grilles, nous les avons comparées à celles mises à disposition par la « Climate Research Unit » de l’Université de « East Anglia » (CRU-UEA). Ces deux types de grilles apparaissent comme très semblables mais les grilles SIEREM sont calculées à partir d’un plus grand nombre de points de mesure et donnent de meilleurs résultats lorsqu’elles sont utilisées en entrée de modèles pluie-débit. Ces grilles sont téléchargeables gratuitement sur le site de la base SIEREM (http://www.hydrosciences.fr/sierem/index.htm).

scholarly journals AVALIAÇÃO DE SIMULAÇÃO HISTÓRICA DA PRECIPITAÇÃO E TEMPERATURA NA AMAZÔNIA ORIENTAL UTILIZANDO UM MODELO DE ALTA RESOLUÇÃO

2019 ◽  
Vol 25 ◽  
Author(s):  
Cláudia Priscila Wanzeler Da Costa ◽  
Everaldo Barreiros De Souza ◽  
Lincoln Muniz Alves ◽  
Luiz Gylvan Meira Filho ◽  
Douglas Batista da Silva Ferreira ◽  
...  
Keyword(s):  
Regional Climate ◽  
Research Unit ◽  
Climate Prediction ◽  
Climate Research Unit ◽  
Climate Research

Este estudo apresenta avaliação do sistema de modelagem climática regional-PRECIS (Providing Regional Climate for Impacts Studies) em simular o clima atual (25 anos, 1981-2005) sobre a Amazônia oriental. As saídas do modelo global HadGEM2-ES foram utilizadas como condições de contorno para o modelo regional dentro do PRECIS, o HadRM3P. Os dados consistiram de médias mensais de precipitação (mm.dia-1) e temperatura do ar (°C.dia-1), a partir das quais obteve-se as médias sazonais. Para a comparação com as simulações fez-se o uso de observações provenientes do CPC (Climate Prediction Centre) e do Climate Research Unit (CRU). O desempenho do modelo foi avaliado através de análises de índices estatísticos como o viés, Raiz do Erro Médio Quadrático (REMQ), coeficiente de correlação, média e desvio padrão. Os resultados mostraram que o modelo reproduz razoavelmente bem os padrões espaciais sazonais da precipitação e temperatura na área de estudo, porém apresenta erros sistemáticos provenientes do HadRM3P, principalmente em DJF (Dezembro-Janeiro-Fevereiro) e MAM (Março-Abril-Maio) no norte (em relação à precipitação) e no leste (à temperatura) da região, respectivamente. Todavia, representou bem a variabilidade temporal da precipitação na porção sul da região, principalmente em MAM, e da temperatura em JJA (Julho-Agosto-Setembro). Os escores estatísticos entre as séries de dados simulados e observados das regiões homogêneas na Amazônia oriental revelaram que o HadRM3P tem melhor acurácia em simular a precipitação em JJA, enquanto a temperatura é melhor representada em SON (Setembro-Outubro-Novembro). Em relação ao ciclo anual nas regiões homogêneas, o modelo regional apresentou melhor desempenho que o global em reproduzir a precipitação, principalmente na estação seca, no entanto, tanto o modelo global quanto o modelo regional tendem a acentuar o ciclo anual da temperatura.

Exploring spatial patterns of trends in monthly rainfall and temperature in the Philippines based on Climate Research Unit grid

2018 ◽  
Vol 26 (5) ◽  
pp. 471-481
Author(s):  
Arnold R. Salvacion ◽  
Damasa B. Magcale-Macandog ◽  
Pompe C. Sta. Cruz ◽  
Ronaldo B. Saludes ◽  
Ireneo B. Pangga ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Research Unit ◽  
The Philippines ◽  
Monthly Rainfall ◽  
Climate Research Unit ◽  
Climate Research

scholarly journals Multi-Criteria Performance Evaluation of Gridded Precipitation and Temperature Products in Data-Sparse Regions

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1597
Author(s):  
Ibrahim Mohammed Lawal ◽  
Douglas Bertram ◽  
Christopher John White ◽  
Ahmad Hussaini Jagaba ◽  
Ibrahim Hassan ◽  
...  
Keyword(s):  
Decision Making ◽  
Performance Metrics ◽  
Research Unit ◽  
Climate Data ◽  
Meteorological Forcing ◽  
Lake Chad ◽  
Climate Research ◽  
Princeton University ◽  
Lake Chad Basin ◽  
Precipitation And Temperature

Inadequate climate data stations often make hydrological modelling a rather challenging task in data-sparse regions. Gridded climate data can be used as an alternative; however, their accuracy in replicating the climatology of the region of interest with low levels of uncertainty is important to water resource planning. This study utilised several performance metrics and multi-criteria decision making to assess the performance of the widely used gridded precipitation and temperature data against quality-controlled observed station records in the Lake Chad basin. The study’s findings reveal that the products differ in their quality across the selected performance metrics, although they are especially promising with regards to temperature. However, there are some inherent weaknesses in replicating the observed station data. Princeton University Global Meteorological Forcing precipitation showed the worst performance, with Kling–Gupta efficiency of 0.13–0.50, a mean modified index of agreement of 0.68, and a similarity coefficient SU = 0.365, relative to other products with satisfactory performance across all stations. There were varying degrees of mismatch in unidirectional precipitation and temperature trends, although they were satisfactory in replicating the hydro-climatic information with a low level of uncertainty. Assessment based on multi-criteria decision making revealed that the Climate Research Unit, Global Precipitation Climatology Centre, and Climate Prediction Centre precipitation data and the Climate Research Unit and Princeton University Global Meteorological Forcing temperature data exhibit better performance in terms of similarity, and are recommended for application in hydrological impact studies—especially in the quantification of projected climate hazards and vulnerabilities for better water policy decision making in the Lake Chad basin.

Space and Time Characteristics of Droughts in Kabul River Basin.

2021 ◽  
Author(s):  
Nadeem Tariq ◽  
Akif Rahim ◽  
Farhan Aziz ◽  
Muhammad Yousaf
Keyword(s):  
River Basin ◽  
Research Unit ◽  
Natural Phenomenon ◽  
Severe Drought ◽  
Standardized Precipitation Evapotranspiration Index ◽  
Climate Research ◽  
Dry Conditions ◽  
Regional Drought ◽  
Year 2000 ◽  
Kabul River

<p>Drought is a complex and less understandable natural phenomenon. Historical characteristics of droughts helps to understand the dynamics of the regional drought patterns. Numerous studies have predicted that the Chitral-Kabul River Basin (CKRB) is prone poses to serious threat due to global warming. This may endanger 10 million in habitants. The aim of this study is to revisit the characteristics of droughts in Kabul watershed, shared by Pakistan and Afghanistan. The monthly Standardized Precipitation-Evapotranspiration Index (SPEI) grided data (0.5<sup>o</sup> 0.5<sup>o</sup>) generated by climate research unit (CRU)version 4 has been used for study during the period 1901–2018. The four characteristics features i.e.  Areal extend, Frequency, Duration and Severity has been studied on spatial and temporal scale. The results show that the Kabul Basin has experienced an increasing extent of severe drought between 1940 and 1960, which increased further after the year 2000. The frequency of drought events in the northern part of the basin is much higher than in the southern part of the basin. Whereas the duration of the drought shows a declining trend in the northern part of the basin. The southern and western parts of the basin experienced a growing trend in the severity drought. At the same time, the incidence of consecutive droughts in the Kabul River basin has also increased. This study suggests that dry conditions in Kabul river basin have been enhanced in recent years. Overall, this study confirms the importance of SPEI for assessing the effects of regional drought.</p><p><strong>Keywords: Drought analysis, Frequency, Severity, Duration, Kabul river basin</strong></p>

scholarly journals Trends in monthly rainfall and temperature in Rachuonyo North Sub-County, Kenya

2016 ◽  
Vol 9 (6) ◽  
pp. 738-747
Author(s):  
E Opere ◽  
S.G. Juma ◽  
B Sitienei
Keyword(s):  
Climate Change ◽  
Seasonal Pattern ◽  
Research Unit ◽  
Descriptive Statistics ◽  
Rainfall Trend ◽  
Resolution Time ◽  
Climate Research ◽  
Increasing Trend ◽  
Test Of Hypothesis ◽  
Change Temperature

We analyse Climate Research Unit (CRU) Precipitation (1961-2014) and Temperature (1985-2014) data trends over Rachuonyo North Sub County at 50×50 km resolution. Time series and correlation analysis of the data was carried out. Temporal characteristics of temperature and Rainfall were determined. One sample t-tailed test of hypothesis was carried out. An increasing trend in temperature is observed over the years while a decreasing trend in precipitation is observed by the year 2014. The trends exhibited a cyclic and Seasonal pattern with Increasing Variability. Descriptive statistics revealed Rainfall and Temperature Means of 119.8 mm and 19.6 °C respectively. The results of one tailed t-test revealed that the change in Rainfall and temperature patterns over the area of study were not statistically significant.Keywords: Climate Change, Temperature, Rainfall Trend, Rachuonyo, Kenya

scholarly journals Evaluasi Data TerraClimate Dalam Mengestimasi Suhu Udara Di Provinsi Papua Barat

2021 ◽  
Vol 14 (1) ◽  
pp. 15-25
Author(s):  
Baso Daeng ◽  
Arif Faisol
Keyword(s):  
Air Temperature ◽  
Research Unit ◽  
Mean Square ◽  
Data Recording ◽  
Climate Research ◽  
West Papua ◽  
Weather Stations ◽  
Papua Barat ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Abstrak. Terra Climate merupakan seperangkat data iklim yang mengkombinasikan antara data WorldClim, Climate Research Unit (CRU), dan Japanese 55-year Reanalysis (JRA 55). TerraClimate menyediakan data iklim bulanan tahun 1958 – 2019  pada resolusi spasial ~4 km. Penelitian ini bertujuan untuk mengevaluasi data TerraClimate dalam mengestimasi suhu udara di Provinsi Papua Barat. Data yang digunakan pada penelitian ini adalah data TerraClimate dan data suhu udara perekaman tahun 1996 – 2019 yang diperoleh dari automatic weather stations (AWS) Rendani – Kabupaten Manokwari, AWS Jefman – Kabupaten Raja Ampat, AWS Torea – Kabupaten Fakfak, dan AWS Kaimana – Kabupaten Kaimana. Data TerraClimate dievaluasi dengan dibandingkan data AWS menggunakan metode point to pixel berdasarkan 5 (lima) parameter statistik, yaitu mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (PBIAS), dan koefisien korelasi Pearson (r). Hasil penelitian menunjukkan bahwa data TerraClimate cenderung overestimated dalam mengestimasi suhu udara minimum bulanan dan cenderung underestimated dalam mengestimasi suhu udara maksimum bulanan di Provinsi Papua Barat. Namun TerraClimate memiliki akurasi yang sangat baik dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat  dengan nilai ME= 0,87 oC, RMSE = 1,19 oC, RBIAS = 0,04, dan PBIAS = 3,71 dalam mengestimasi suhu udara minimum, dan ME = 0,54 oC, RMSE = 0,88 oC,  RBIAS = 0,02, dan PBIAS = 1,79 dalam mengestimasi suhu udara maksimum. Disamping itu TerraClimate memiliki korelasi yang sedang terhadap data AWS nilai r = 0,40 - 0,68. Sehingga TerraClimate dapat digunakan sebagai solusi alternatif untuk penyedia data suhu udara di Provinsi Papua Barat.An Evaluation of TerraClimate Data in Estimating Monthly Air Temperature in West PapuaAbstract. TerraClimate is a climate dataset that combines WorldClim data, Climate Research Unit (CRU) data, and Japanese 55-year Reanalysis (JRA 55) data at ~4 km spatial resolution. TerraClimate provides monthly climate data from 1958 to recent years. This research aims to evaluate the TerraClimate data in estimating monthly air temperature in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are TerraClimate data and AWS data recording from 1996 to 2019 obtained from AWS Rendani – Manokwari, AWS Jefman – Raja Ampat, AWS Torea – Fakfak, and AWS Kaimana – Kaimana. TerraClimate data were evaluated using the Point to Pixel method based on 5 (five) statistical parameters i.e., mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (RBIAS), and Pearson correlation coefficient (r). The research showed that TerraClimate is overestimated in estimating monthly minimum air temperature and underestimated in estimating monthly maximum air temperature in West Papua. However, TerraClimate and has very good accuracy in estimating the monthly temperature in West Papua with ME = 0.87 oC, RMSE = 1.19 oC, RBIAS = 0.04, and PBIAS = 3.71 in estimating monthly minimum air temperature, and ME=0.54 oC, RMSE = 0.88 oC, RBIAS = 0.02, PBIAS = 1.79 in estimating monthly maximum air temperature. Besides, TerraClimate data has a moderate correlation with AWS data in estimating monthly air temperature with r= 0.40 - 0.68. Therefore, TerraClimate can be used as an alternative solution for providing air temperature data in West Papua. 

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