Tufa Dinku
Columbia University
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Publication
Featured researches published by Tufa Dinku.
Journal of remote sensing | 2007
Tufa Dinku; Pietro Ceccato; Emily K. Grover-Kopec; M. Lemma; Stephen J. Connor; Chester F. Ropelewski
An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA‐CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM‐3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3‐hourly to 10‐daily) resolution. These included the NOAA‐CPC African rainfall estimation algorithm, GPCP one‐degree‐daily (1DD), TRMM‐3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10‐day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM‐3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM‐3B42 from the second group performed reasonably well.
Journal of remote sensing | 2008
Tufa Dinku; S. Chidzambwa; Pietro Ceccato; Stephen J. Connor; Chester F. Ropelewski
High‐resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA‐CPC African rainfall estimation algorithm (RFE), TRMM‐3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratorys blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM‐3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM‐3B42 preformed relatively better over Zimbabwe.
Journal of Applied Meteorology and Climatology | 2010
Tufa Dinku; Franklyn Ruiz; Stephen J. Connor; Pietro Ceccato
Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation m...
Archive | 2010
Tufa Dinku; Stephen J. Connor; Pietro Ceccato
Two satellite rainfall estimation algorithms, CMORPH and TMPA, are evaluated over two mountainous regions at daily accumulation and spatial resolution 0.25°. The evaluated TMPA products are TRMM-3B42 and TRMM-3B42RT. The first of the two validations region is located over the Ethiopian highlands in the Horn of Africa. The second is located over the highlands of Columbia in South America. Both sites are characterized by a very complex terrain. Relatively dense station networks over the two sites are used to validate the satellite products. The correlation coefficients between the reference gauge data and the satellite products were found to be low. Besides, the products underestimate both the occurrence and amount of rainfall over both validation sites. These were attributed, at least partly, to orographic warm rain process over the two regions. The performance over Colombia was better compared to that for Ethiopia. And CMORPH has exhibited better performance as compared to the two TRMM products.
International Journal of Remote Sensing | 2011
Tufa Dinku; Pietro Ceccato; Stephen J. Connor
Different satellite rainfall products are used in different applications over different parts of the world. These products are particularly important over many parts of Africa, where they are used to augment the very sparse rain-gauge network. However, the quality of the different satellite products varies from one product to another and from one climatic region to another. The climate over eastern Africa varies from wet coastal and mountainous regions to dry arid regions. Significant variations could be observed within short distances. The different climates will pose different challenges to satellite rainfall retrieval over this region. This study explores the effect of mountainous and arid climates on four different satellite rainfall-estimation (RFE) algorithms. The mountainous climate is located over the Ethiopian highlands, while the arid region covers parts of Ethiopia, Djibouti and Somalia. One infrared-only product, African rainfall climatology (ARC), one passive-microwave-only product (the Climate Prediction Center morphing technique, CMORPH) and two products (the RFE algorithm and the tropical rainfall measuring mission (TRMM-3B42)), which combine both infrared and passive-microwave estimates, are used for this investigation. All the products exhibit moderate underestimation of rainfall over the highlands of Ethiopia, while the overestimation over the dry region is found to be very high. The underestimation over the mountainous region is ascribed to the warm orographic rain process, while the overestimation over the dry region may be because of sub-cloud evaporation. Local calibration of satellite algorithms and merging of satellite estimates with all locally available rain-gauge observations are some of the approaches that could be employed to alleviate these problems.
Journal of Applied Meteorology and Climatology | 2010
Tufa Dinku; Pietro Ceccato; Keith Cressman; Stephen J. Connor
Abstract This paper evaluates rainfall detection capabilities of seven satellite rainfall estimates over the desert locust recession regions of the world. The region of interest covers the arid and semiarid region from northwestern Africa to northwestern India. The evaluated satellite rainfall products are the African rainfall climatology (ARC), rainfall estimation algorithm (RFE), Tropical Rainfall Measuring Mission 3B42 and its real-time version (3B42RT), NOAA/Climate Prediction Center morphing technique (CMORPH), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP-MVK and GSMaP-MVK+). The reference data were obtained from the Desert Locust Information Service of the United Nations Food and Agriculture Organization (FAO). The FAO data are qualitative information collated by desert locust survey teams from different countries during field campaigns. Such data can only be used to assess the rainfall detection capabilities of the satellite products. The...
Earth Perspectives | 2014
Tufa Dinku; Paul Block; Jessica Sharoff; Kinfe Hailemariam; Daniel E. Osgood; John del Corral; Rémi Cousin; Madeleine C. Thomson
Sound climate risk management requires access to the best available decision-relevant climate information and the ability to use such information effectively. The availability and access of such information and the ability to use it is challenging, particularly throughout rural Africa. A gap analysis published by the International Research Institute for Climate and Society (IRI) and the Global Climate Observing System (GCOS) in 2005 explored these challenges in detail and identified four key gaps: (i) gaps in integration of climate into policy; (ii) gaps in integration of climate into practice at scale; (iii) gaps in climate services; and (iv) gaps in climate data. Though this document was published nearly nine years ago, the gaps it highlighted are still relevant today. In the last decade, IRI has been making efforts to address these critical issues in a systematic way through projects and partnerships in Africa. This paper describes IRI’s efforts in Ethiopia, a country particularly prone to climate related risks. Here we outline a creative solution to bridge the gaps in the availability, access and use of national climate information through the Enhancing National Climate Services (ENACTS) initiative. We then discuss how policy and practice has changed as a result of IRI engagement in the development of climate services in the water, public health and agricultural sectors. The work in Ethiopia is indicative of the efforts IRI is implementing in other countries in Africa and in other parts of the world.
Water Resources Research | 2014
Tsegaye Tadesse; Getachew B. Demisse; Ben Zaitchik; Tufa Dinku
An experimental drought monitoring tool has been developed that predicts the vegetation condition (Vegetation Outlook) using a regression-tree technique at a monthly time step during the growing season in Eastern Africa. This prediction tool (VegOut-Ethiopia) is demonstrated for Ethiopia as a case study. VegOut-Ethiopia predicts the standardized values of the Normalized Difference Vegetation Index (NDVI) at multiple time steps (weeks to months into the future) based on analysis of “historical patterns” of satellite, climate, and oceanic data over historical records. The model underlying VegOut-Ethiopia capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Nino and the Southern Oscillation (ENSO)) expressed over the 24 year data record and also considers several environmental characteristics (e.g., land cover and elevation) that influence vegetations response to weather conditions to produce 8 km maps that depict future general vegetation conditions. VegOut-Ethiopia could provide vegetation monitoring capabilities at local, national, and regional levels that can complement more traditional remote sensing-based approaches that monitor “current” vegetation conditions. The preliminary results of this case study showed that the models were able to predict the vegetation stress (both spatial extent and severity) in drought years 1–3 months ahead during the growing season in Ethiopia. The correlation coefficients between the predicted and satellite-observed vegetation condition range from 0.50 to 0.90. Based on the lessons learned from past research activities and emerging experimental forecast models, future studies are recommended that could help Eastern Africa in advancing knowledge of climate, remote sensing, hydrology, and water resources.
international geoscience and remote sensing symposium | 2006
Pietro Ceccato; Michael Bell; Martin Benno Blumenthal; Stephen J. Connor; Tufa Dinku; Emily K. Grover-Kopec; Chester F. Ropelewski; Madeleine C. Thomson
A number of the major human infectious diseases (like malaria and dengue) and Desert Locusts that still plague the developing world are sensitive to inter-seasonal and inter-decadal changes in environment and climate. Monitoring variations in environmental conditions such as rainfall and vegetation helps decision-makers at Ministries of Agriculture and Ministries of Health to assess the risk levels of Desert Locust outbreaks or malaria epidemics. The International research institute for climate and society (IRI) has developed products based on remotely sensed data to monitor those changes and provide the information directly to the decision-makers. This paper presents recent developments which use remote sensing to monitor climate variability, environmental conditions and their impacts on the dynamics of infectious diseases (malaria) and Desert Locust outbreaks.
International Journal of Environmental Research and Public Health | 2014
Michel Jancloes; Madeleine C. Thomson; María Máñez Costa; Chris Hewitt; Carlos Corvalan; Tufa Dinku; Rachel Lowe; Mary H. Hayden
A high level expert panel discussed how climate and health services could best collaborate to improve public health. This was on the agenda of the recent Third International Climate Services Conference, held in Montego Bay, Jamaica, 4–6 December 2013. Issues and challenges concerning a demand led approach to serve the health sector needs, were identified and analysed. Important recommendations emerged to ensure that innovative collaboration between climate and health services assist decision-making processes and the management of climate-sensitive health risk. Key recommendations included: a move from risk assessment towards risk management; the engagement of the public health community with both the climate sector and development sectors, whose decisions impact on health, particularly the most vulnerable; to increase operational research on the use of policy-relevant climate information to manage climate- sensitive health risks; and to develop in-country capacities to improve local knowledge (including collection of epidemiological, climate and socio-economic data), along with institutional interaction with policy makers.