Developing High-Resolution Gridded Rainfall and Temperature Data for Bangladesh: The ENACTS-BMD Dataset

This manuscript describes the construction and validation of high resolution daily gridded (0.05° × 0.05°) rainfall and maximum and minimum temperature data for Bangladesh : the Enhancing National Climate Services for Bangladesh Meteorological Department (ENACTS-BMD) dataset. The dataset was generated by merging data from weather stations, satellite products (for rainfall) and reanalysis (for temperature). ENACTS-BMD is the first high-resolution gridded surface meteorological dataset developed specifically for studies of surface climate processes in Bangladesh. Its record begins in January 1981 and is updated in real-time monthly and outputs have daily, decadal and monthly time resolution. The Climate Data Tools (CDT), developed by the International Research Institute for Climate and Society (IRI), Columbia University, is used to generate the dataset. This data processing includes the collection of weather and gridded data, quality control of stations data, downscaling of the reanalysis for temperature, bias correction of both satellite rainfall and downscaled reanalysis of temperature, and the combination of station and bias-corrected gridded data. The ENACTS-BMD dataset is available as an open-access product at BMD’s official website, allowing the enhancement of the provision of services, overcoming the challenges of data quality, availability, and access, promoting at the same time the engagement and use by stakeholders.

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ENACTS: Transforming Climate Services Across Africa

10.5194/egusphere-egu21-39 ◽

2021 ◽

Author(s):

Tufa Dinku

Keyword(s):

Effective Means ◽

National Level ◽

Columbia University ◽

Climate Information ◽

Climate Data ◽

Climate Services ◽

Spatial And Temporal Scales ◽

Institutional Infrastructure ◽

International Research Institute ◽

Information Products

<p>Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University&#8217;s International Research Institute for Climate and Society, strives to overcome these challenges by targeting the way climate-sensitive decisions are made at the local, regional, and national levels. The ENACTS approach is executed by working directly with the National Meteorological and Hydrological Services (NMHS) to build capacity for improving the availability, access, and use of quality climate data and information products at relevant spatial and temporal scales. The ENACTS approach has shown to be an effective means to transform decision-making surrounding vulnerabilities and risks at both national and local scales in over a dozen countries at the national level as well as at regional level East and West Africa. In the ENACTS approach, challenges to the availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products are promoted through raising awareness in relevant communities, training users, and co-production processes.</p>

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Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

Geoscientific Model Development ◽

10.5194/gmd-13-6349-2020 ◽

2020 ◽

Vol 13 (12) ◽

pp. 6349-6360

Author(s):

Zhiqiang Li ◽

Bingcheng Wan ◽

Yulun Zhou ◽

Hokit Wong

Keyword(s):

Quality Control ◽

Hong Kong ◽

High Resolution ◽

Data Quality ◽

Land Surface ◽

Urban Climate ◽

Data Quality Control ◽

Climate Simulations ◽

Arw Model ◽

Hong Kong Sar

Abstract. The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

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Incoming data quality control in high-resolution urban climate simulation: Hong Kong-Shenzhen area urban climate simulation as a case study using WRF/Noah LSM/SLUCM model (Version 3.7.1)

10.5194/gmd-2020-45 ◽

2020 ◽

Author(s):

Zhiqiang Li ◽

Bingcheng Wan ◽

Yulun Zhou ◽

Hokit Wong

Keyword(s):

Quality Control ◽

Hong Kong ◽

High Resolution ◽

Data Quality ◽

Land Surface ◽

Urban Climate ◽

Climate Simulation ◽

Data Quality Control ◽

Climate Simulations ◽

Hong Kong Sar

Abstract. Growing computational power in recent years enabled high-resolution urban climate simulations using limited-area models to flourish. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial-temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity, and these artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely-applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen was going through an unprecedented urbanization process in the past thirty years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data are found out-dated, which highlights the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Then, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the reliability of modeling results. The reliability of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence on the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

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ENACTS: Advancing Climate Services Across Africa

Frontiers in Climate ◽

10.3389/fclim.2021.787683 ◽

2022 ◽

Vol 3 ◽

Author(s):

Tufa Dinku ◽

Rija Faniriantsoa ◽

Remi Cousin ◽

Igor Khomyakov ◽

Audrey Vadillo ◽

...

Keyword(s):

Multiple Scales ◽

Effective Means ◽

National Level ◽

Climate Information ◽

Climate Data ◽

Climate Services ◽

Spatial And Temporal Scales ◽

Institutional Infrastructure ◽

International Research Institute ◽

Information Products

Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University's International Research Institute for Climate and Society (IRI), strives to overcome these challenges by co-developing tailored, actionable, and decision-relevant climate information with and for a wide variety of users at the local, regional, and national levels. This is accomplished through an approach emphasizing direct engagement with the National Meteorological and Hydrological Services (NMHS) and users of their products, and investments in both technological and human capacities for improving the availability, access, and use of quality climate data and information products at decision-relevant spatial and temporal scales. In doing so, the ENACTS approach has been shown to be an effective means of transforming decision-making surrounding vulnerabilities and risks at multiple scales, through implementation in over a dozen countries at national level as well as at the regional levels in both East and West Africa. Through the ENACTS approach, challenges to availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products is promoted through raising awareness in relevant communities, training users, and co-production processes.

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Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set

Scientific Data ◽

10.1038/s41597-020-00643-7 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 2

Author(s):

Andrew Verdin ◽

Chris Funk ◽

Pete Peterson ◽

Martin Landsfeld ◽

Cascade Tuholske ◽

...

Keyword(s):

High Resolution ◽

Remotely Sensed ◽

Temperature Data ◽

Daily Temperature ◽

Good Representation ◽

Climate Data ◽

Data Set ◽

Weather Forecasts ◽

Development And Validation

Abstract We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.

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Transforming Access to and Use of Climate Information Products Derived from Remote Sensing and In Situ Observations

Remote Sensing ◽

10.3390/rs13224721 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4721

Author(s):

Gloriose Nsengiyumva ◽

Tufa Dinku ◽

Remi Cousin ◽

Igor Khomyakov ◽

Audrey Vadillo ◽

...

Keyword(s):

Remote Sensing ◽

Decision Making ◽

Information Needs ◽

Climate Information ◽

Climate Data ◽

Climate Services ◽

International Research Institute ◽

Combining Data ◽

Information Products ◽

User Friendly

Making climate-sensitive economic sectors resilient to climate trends and shocks, through adaptation to climate change and managing uncertainties associated with climate extremes, will require effective use of climate information to help practitioners make climate-informed decisions. The provision of weather and climate information will depend on the availability of climate data and its presentation in formats that are useful for decision making at different levels. However, in many places around the world, including most African countries, the collection of climate data has been seriously inadequate, and even when available, poorly accessible. On the other hand, the availability of climate data by itself may not lead to the uptake and use of such data. These data must be presented in user-friendly formats addressing specific climate information needs in order to be used for decision-making by governments, as well as the public and private sectors. The generated information should also be easily accessible. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society (IRI), has been making efforts to overcome these challenges by supporting countries to improve the available climate data, as well as access to and use of climate information products at relevant spatial and temporal scales. Challenges to the availability of climate data are alleviated by combining data from the national weather observation network with remote sensing and other global proxies to generate spatially and temporally complete climate datasets. Access to climate information products is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products such as maps and graphs.

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