Processing NOAA Observation Data over Hybrid Computer Systems for Comparative Climate Change Analysis
|
|
- Darcy Briggs
- 6 years ago
- Views:
Transcription
1 Processing NOAA Observation Data over Hybrid Computer Systems for Comparative Climate Change Analysis Xuan Shi 1,, Dali Wang 2 1 Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA 2 Environmental Science Division, Oak Ridge National Lab Oak Ridge TN 37831, USA Abstract - With the rapid development of weather monitoring system, numerous observational data are available. For example, NOAA provides Global Surface Summary of Day (GSOD) data that incorporates daily weather measurements from over 9000 weather stations around the world. In this paper, a comprehensive workflow and methodology is presented to elaborate how to transform GSOD data into a new and useful format so as to generate interpolated product of daily, monthly and annual mean surface temperature datasets by using advanced computation platforms. The quality of this gridded, high resolution (at ¼ degree) daily product is further examined by comparing to an existing global climate dataset. A preliminary comparison on global surface temperature shows a consistent agreement between these two datasets, with the major differences located in a few regions. The interpolated GSOD data products are supplementary to existing datasets by providing new gridded, high resolution observation-based daily temperature information over three decades ( ), which are very useful for decadal climate change researches. Keywords: GSOD/NOAA Observational Data, Interpolation, Parallel Computing, GPU 1 Introduction Historical weather datasets have been extensively used as a source of information to study global climate change and to validate and verify earth system models [1][2]. NOAA provides Global Surface Summary of the Day (GSOD) data through an FTP server ftp://ftp.ncdc.noaa.gov/pub/data/gsod/. GSOD data incorporates daily weather measurements (temperature, dew point, wind speed, humidity, barometric pressure, and so forth) from over 9000 weather stations around the world. GSOD data are available from 1929 to the present, with the data from 1973 to the present being the most complete. GSOD data, in its original format, however, could hardly be utilized directly and efficiently by researchers. Since each weather station is identified by a specific code, it is difficult to know where those stations are located in the archived data files when the location [latitude and longitude] information of weather observation station, as well as other descriptive information about the data, is documented in the metadata file, which is stored separately from the source data in ASCII format. This means, even the users can download the data from the FTP Server, it is difficult for them to understand where the weather stations are located if they cannot link the location information within the metadata with the ASCII file, let alone to find station(s) for a specific area or place. It is impossible to derive the global climate change information from such individual, station-based, unstructured data for any given temporal scope. This paper describes the workflow and method to transform GSOD raw data into an applicable format and how to produce interpolated temperature data from daily meteorological observations at any arbitrary resolution. Considering the general interest of journal readership, global daily temperature results on a 0.25 degree x 0.25 degree surface grid were generated for duration from 1982 to Furthermore, time-series monthly average temperature grids were generated to compare with widely used high resolution gridded datasets ( which contain the time series of monthly average temperature developed by the Climatic Research Unit at the University of East Anglia (CRU TS). The preliminary comparison result indicates the overall consistence between these two datasets, while major differences are located around the Tibet plateau. The future work will focus on the improvements of station selection for interpretation, the topography-dependent heterogeneity of surface temperature measurement, as well as comparison with other existing global datasets, such as NASA Goddard Institute for Space Studies (GISS) and Moderate Resolution Imaging Spectroradiometer) MODIS datasets. 2 Methodology and Workflow GSOD data contains a variety of observed weather information including the mean, maximum and minimum temperature, mean dew point, mean sea level and station pressure, precipitation amount and snow depth, as well as other elements. However, such an invaluable data has been archived by individual station in unstructured ASCII format. In order to efficiently utilize GSOD data for climate change research, station-based data has to be transformed into datebased data in which the location of the station is embedded
2 and merged into the time series datasets. As a result, daily global mean surface temperature can be approximated by applying interpolation algorithm. Furthermore, monthly and annual mean surface temperature as well as anomaly can be developed. Interpolation is a method to estimate the value of unsampled location based on the values of existing observations. Interpolation can be implemented by different approaches in different domain science applications. Among these approaches, Kriging is a geostatistical interpolation method that is effective for predicting the spatial distributions of geographic features, although Kriging has complex implementation and a large computation load [3][4][5][6]. We applied Kriging interpolation in this pilot study. Technically, the data processing workflow contains four steps (as shown in Figure 1), including 1) data transformation, which converts station-based ASCII file into date-based data by integrating the location information into the new daily dataset; 2) data interpolation, which generates interpolated daily mean surface temperature; 3) data aggregation, which generate the monthly and annual mean surface temperature or 30 year anomaly for example; and 4) data subtraction, which derives the temperature change information. Scientifically, the new data product will help user and researchers to 1) identify and understand the spatial and temporal differentiation of climate change in the past decades at the global and local scale; 2) explore and understand the climate change tendency by analyzing and visualizing historical data; 3) compare, validate or examine potential climate models and results; 4) integrate the output into other research projects as the source of data. 3 Computational Platform Kriging interpolation is data and compute intensive especially when great circle distance is applied to identify a given number of nearest neighboring observation stations. When high resolution output grids are generated, it may take hundreds of days to process the entire data for three decades. Parallel computing over the Graphic Processing Units (GPU) can significantly accelerate the time-consuming calculation process to improve the performance in verities of scientific computation. When multiple GPUs can be utilized to accelerate Kriging calculation, the processing time can be reduced from dozens of minutes on the serial program to dozens of seconds on a single GPU over a desktop computer and to a few seconds on Keeneland [7], which is a hybrid computer system that has 240 CPUs and 360 GPUs. We started the development process to implement Kriging computation over a desktop computer in order to establish a standard for quality control and performance comparison to the parallel solution and products. The desktop computer has an Intel Pentium 4 CPU with 3.00 GHz main frequency, while the RAM size is 4 GB. The desktop machine has a graphic processing unit (GPU) that is a NVIDIA GeForce GTS 450, which has 192 cores and has 1 GB global memory. According to the technical specification, this GPU has 24 streaming multiprocessors (SM). Each SM has 8 CUDA cores called as streaming processor (SP). In this GTS 450 with a compute capability of 2.1, up to 1024 threads can be assigned to each SM. Thus a maximum of 1024 x 24 = 24,576 threads can run concurrently in parallel on the physical GPU, although the maximum sizes of each dimension of a block is 512 x 512 x 64 and the maximum Figure 1: Workflow of GSOD data transformation and computation
3 sizes of each dimension of a grid is x x 1. If the number of threads is more than the maximum number [24,576], the remaining threads have to wait. After the Kriging interpolation algorithm is implemented and validated over both the serial program and the parallel solution over desktop GPU, Kriging is implemented over Keeneland, a hybrid computer system jointly developed by Georgia Institute of Technology, the University of Tennessee at Knoxville and the Oak Ridge National Laboratory sponsored by NSF, to accelerate the computation over more than 10,000 daily temperature estimation. Keeneland is composed of an HP SL-390 (Ariston) cluster with Intel Westmere hex-core CPUs, NVIDIA 6GB Fermi GPUs, and a Qlogic QDR InfiniBand interconnect. The system has 120 nodes, each with two CPUs and three GPUs, while all CPUs and GPUs are bridged together through one I/O hub from which the CPUs can read/write data. Generally the CPUs serve as a high-level controller coordinated through Message Passing Interface (MPI), while GPUs implement the intensive computation job at a relatively low-level. By utilizing multiple GPUs on Keeneland, the computational time on interpolation was reduced to 3-4 seconds when one Keeneland node with three GPUs was utilized even without applying any spatial index 4 Implementation Details Within the workflow of data transformation and computation, Kriging interpolation could be the most timeconsuming procedure when a resolution of 0.25 x 0.25 degree grid is designed as the output product that has 1440 x 720 = 1,036,800 cells. If the GSOD data has 2,000+ to 5,000+ records, Kriging through serial computer program needs more than 11 or 30 minutes. While the weather observational stations in the world have been increasing in recent decades, GSOD data may have more than 10,000 daily records thus Kriging may need minutes to process one daily data. If interpolating one day data needs 30 minutes by average through serial program, it would take about 328,500 minutes or 228 days to process 30 years of daily data from 1982 to For this reason, we pursued high performance computing solution by utilizing hybrid computer system to implement kriging through combined message Passing Interface (MPI) and Graphics processing unit (GPU) programs. 4.1 Implementation on desktop GPU GPU was traditionally utilized in computer graphics applications. Considering the massive parallelism enabled by the GPU, it can be used for general-purpose computing and thus called GPGPU. By executing tens of thousands of threads concurrently, GPGPU enables high performance computing even on desktop or laptop computers. Compute Unified Device Architecture (CUDA) is NVIDIA s generalpurpose parallel computing architecture. Here, the Central Processing Unit (CPU) is referred to as a host, while an individual GPU is referred to as a device. Normally the GPU executes the data computation process while I/O is done on the CPU, which also manipulates the workflow. The kernel is the function that runs on the device and is executed by an array of threads, while all threads can run the same code concurrently. Each thread has a unique thread identifier and can be accessed via the threadidx variable. Thread identifiers (threadids) can be defined in one, two or three dimensions. Furthermore, threads can be grouped into thread blocks and grids. Threads in same thread block can cooperate with each other via shared memory, atomic operations or barrier synchronization. Threads in different blocks cannot cooperate. A user-defined number of threads can be organized in a block with a maximum number of 512 threads. Similarly a group of thread blocks can be organized into a grid in which each thread may be executed independently and thus may execute in parallel. The first test is on the desktop computer through Visual Studio.Net Interpolation calculation could be a perfect match for parallel computing using GPUs. In essence, interpolation can be treated as a matrix calculation which is generic in GPGPU applications. We specify a number of columns and rows to define the dimension of the output grid. For each cell, we need to first find a given number of nearest neighboring points that have observational records. Then we implement the Kriging algorithm over each cell for interpolation calculation to derive the approximated value based on the observational values of its nearest neighbors. The calculation on each cell has no dependence on the other cells thus interpolation can be processed as an embarrassingly parallelism. A general scheme for Kriging by CUDA C program can be summarized as: 1. Specify the types and sizes of input and output data; 2. Allocate memory on GPU for input data, output data, and intermediate data; 3. Allocate the computing resource on GPU, i.e. specify number of threads per block and total number of blocks; 4. Copy both input and output data from CPU to GPU; 5. Execute the algorithm for kriging computation; 6. Copy both input and output data from GPU to CPU; 7. Write the output data in ASCII grid format; 8. Free the allocated GPU memory. To achieve high performance, we specify the number of blocks to be used and the number of threads in each block. In this case, for example, if 20,000 concurrent threads can be used to run Kriging interpolation, the program will be executed 50 times if the output grid has 1 million cells.
4 Table 1. Performance comparison based on different scale of data between serial program, a single GPU over desktop machine, as well as combined MPI and GPU program over Keeneland. Time is counted in seconds. Data Size Time/speedup on desktop Time/Speedup on Keeneland 1 CPU 1 GPU 1 GPU 3 GPUs 6 GPUs 9 GPUs / 12 7 / 96 4 / / / / 24 8 / / / / / 30 7 / / / / / 53 6 / / / / Implementation on Keeneland Implementing the spherical interpolation computation on Keeneland is a combination of MPI and CUDA programs. The CUDA program is responsible for the computation of a block of the interpolated raster grid divided by horizontal rows. Each MPI process has a unique process rank number which is used to specify how many and which rows each CUDA program will process on the GPU node. The MPI processes read the input data, assign the jobs to the GPU nodes to implement the spherical interpolation program, and write a segment of the output data into a file in parallel. When all MPI processes are completed, one MPI process merges all segments of the output data into a single file. 5 Performance Evaluation A varied scale of datasets is used in the performance testing. Given the size of the output grid as 1440 x 720 = 1,036,800 cells, Tables 1 displays the performance of the Kriging interpolation over different datasets using a single CPU, and the performance and speedups of the parallelized solutions on a single GPU on a desktop computer, and on 1, 3, 6, and 9 GPUs on Keeneland. In this case, 10 nearest neighboring points that have observational values are used in the Kriging calculation. The advantage of using the GPU is noticeable along with the increasing data scale as the speedup increases even when a single GPU is used on a desktop computer. In Keeneland, the maximum speedup is achieved when one node with 3 GPUs was utilized at all scales of input data. This result implies that at current scale of input data size, utilizing more GPUs may result in more overhead for data manipulation between the host and device. If larger scale of data could be applied, it may have a different performance pattern or result over different number of GPUs utilized in the calculation. 6 Visualization and Analytics of Interpolated GSOD Data Now that GSOD data has been transformed into the new format, such observational data can be visualized and analyzed through geographic information system (GIS) software for example. Figure 2 displays the distribution of the weather observation stations on the globe on July 1, 2009 and the global mean surface temperature on this specific day modeled by Kriging calculation. Time series of temperature evolution can be visualized as a movie or animation. For any given location on the earth, we can to search query on the GSOD data through the identification function call. By clicking on the map interface, we can identify the location (latitude and longitude) of the clicked point and retrieve the daily mean surface temperature of this given location for any year. In this way, we can examine the quality of the Kriging result by comparing the Kriging result with the original GSOD data for any known station so as to validate the methodology for further improvement of this work on the one hand. On the other hand, we can offer the capability or service to allow users to search query over the local or Figure 2: Distribution of weather observation stations and the global mean surface temperature on 07/01/2009
5 Figure 3: Global temperature profiles in winter month (Jan, 2006) (left: CRU TS data, right: gridded GSOD product with the same color scheme). Figure 4: Global temperature profiles in summer month (July 2006) (left: CRU TS data, right: gridded GSOD product with the same color scheme). regional temperature change for a certain time period to enhance the domain science research and application development. 7 Comparison with Existing Global Climate Dataset 7.1 Dataset description Climatic Research Unit (CRU) TS (time-series), or CRU TS, datasets contain month-by-month variations of global climate information over the last century or so. CRU TS datasets are archived as high-resolution (0.5 x 0.5 degree) grids of monthly mean temperatures derived from more than 4000 weather stations distributed around the world. CRU TS data includes weather information such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapor pressure and wet day frequency. At present, the British Atmospheric Data Center holds the CRU TS 3.0 datasets for the period as well as the CRU TS 3.1 datasets for the period In this study, the monthly temperature dataset from CRU TS 3.0 [8] is used for the comparison to the gridded daily product of GSOD temperature. 7.2 Comparison Since CRU TS only has half degree grids of monthly average temperature over six earth continents (without Antarctica), we filtered out all the data covering the ocean and Antarctica for comparison to the monthly mean surface temperature derived from the interpolated GSOD daily mean surface temperature over those six continents for comparison. Figures 3 and 4 display the spatial distribution of the average temperature of two specific months, specified by CRU TS dataset in winter and summer month (January and July) in As shown in Figure 3 and 4, the temperature distribution pattern exemplifies a good match between CRU TS data and our gridded, GSOD product in a majority of areas, while major difference exists around Himalaya mountain areas. 8 Conclusions and Future Work This paper presented the method and workflow to transform NOAA Global Surface Summary of Day (GSOD) data into a more useful format to support climate change research. While the location of weather observation stations is embedded, date-based GSOD data can be further transformed into detailed gridded data products at very fine spatial and temporal scale. By deploying hybrid computer architecture and systems, interpolating global daily mean surface temperature in the past 30 years can be accomplished within two hours. The quality of interpolated GSOD products exemplifies satisfied quality in most regions over the continents in the world. The preliminary comparison between CRU TS data and our new gridded data products derived from GSOD shows a consistent match between these two datasets, with the major difference identified around boundaries of Tibetan plateau. With the increasing demands on the research of decadal climate change and its impact, our gridded GSOD data products can serve as a high fidelity
6 benchmark datasets to validate and verify those finer scale climate simulation results. It can also be used as fine scale (both temporal and spatial) external forcing to investigate regional climate impacts. The future work will focus on the improvements of station selection for interpretation, and the topography-dependent heterogeneity of surface temperature measurement in the data generation procedure. Further comparison with other existing global climate datasets, such as NASA GISS datasets ( and MODIS datasets (lpdaac.usgs.gov), will help to understand the different models and the output results for climate change research. Our gridded GSOD data product [i.e. global daily mean surface temperature grids at a resolution of 0.25 degree x 0.25 degree for a duration between 01/01/1982 and 12/31/2011] is now available upon request, and authors are making plans to make the product available via Distributed Active Archive Center for Biogeochemical Dynamics at Oak Ridge National Laboratory. All the datasets generated by this study are available upon request, and DOI was requested for those datasets. 9 Acknowledgement This research was supported partially by the National Science Foundation through the award OCI This research used resources of the Keeneland Computing Facility at the Georgia Institute of Technology, which is supported by the National Science Foundation under Contract OCI Oak Ridge National Laboratory is managed by UT- Battelle LLC for the Department of Energy under contract DE-AC05-00OR [5] Cheng, T.; Zhang, Y.; Li, D.; Wang, Q. 2010, A component-based design and implementation for parallel Kriging library, Information Science and Engineering (ICISE), nd International Conference on, vol., no., pp.1-4, 4-6 Dec [6] Srinivasan, B. V.; R. Duraiswami; and R. Murtugudde Efficient kriging for real-time spatio-temporal interpolation. Online proceedings of the 20th Conference on Probability and Statistics in the Atmospheric Sciences. [7] Vetter, J.S., R. Glassbrook, J. Dongarra, K. Schwan, B. Loftis, S. McNally, J. Meredith, J. Rogers, P. Roth, K. Spafford, and S. Yalamanchili, Keeneland: Bringing heterogeneous GPU computing to the computational science community, IEEE Computing in Science and Engineering, 13(5):90-5, [8] CRUNECP, Mitchell, T.D. and Jones, P.D., 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology 25, doi: /joc References [1] National Research Council. A National Strategy for Advancing Climate Modeling. Washington, DC: The National Academies Press, [2] Trenberth, K. E., Anthes, R. A., Belward, A., Brown, O., Haberman, E., Karl, T. R., Running, S., Ryan, B., Tanner, M., and Wielicki, B., 2012: Challenges of a sustained climate observing system. In Climate Science for Serving Society: Research, Modelling and Prediction Priorities, Hurrell, J. W. and Asrar, G. eds., Springer, accepted. [3] Oliver, M. A. and R. Webster Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Science, Vol. 4, No. 3. (1990), pp [4] Tang, Tao Spatial Statistic Interpolation of Morphological Factors for Terrain Development. GIScience & Remote Sensing. Volume 42, Number 2 / April-June pp
Scalable and Power-Efficient Data Mining Kernels
Scalable and Power-Efficient Data Mining Kernels Alok Choudhary, John G. Searle Professor Dept. of Electrical Engineering and Computer Science and Professor, Kellogg School of Management Director of the
More informationA Cloud-Based Flood Warning System For Forecasting Impacts to Transportation Infrastructure Systems
A Cloud-Based Flood Warning System For Forecasting Impacts to Transportation Infrastructure Systems Jon Goodall Associate Professor, Civil and Environmental Engineering Associate Director, Link Lab April
More informationWelcome to MCS 572. content and organization expectations of the course. definition and classification
Welcome to MCS 572 1 About the Course content and organization expectations of the course 2 Supercomputing definition and classification 3 Measuring Performance speedup and efficiency Amdahl s Law Gustafson
More informationIntroduction to IsoMAP Isoscapes Modeling, Analysis, and Prediction
Introduction to IsoMAP Isoscapes Modeling, Analysis, and Prediction What is IsoMAP To the user, and online workspace for: Accessing, manipulating, and analyzing, and modeling environmental isotope data
More informationUsing a CUDA-Accelerated PGAS Model on a GPU Cluster for Bioinformatics
Using a CUDA-Accelerated PGAS Model on a GPU Cluster for Bioinformatics Jorge González-Domínguez Parallel and Distributed Architectures Group Johannes Gutenberg University of Mainz, Germany j.gonzalez@uni-mainz.de
More informationThe Performance Evolution of the Parallel Ocean Program on the Cray X1
The Performance Evolution of the Parallel Ocean Program on the Cray X1 Patrick H. Worley Oak Ridge National Laboratory John Levesque Cray Inc. 46th Cray User Group Conference May 18, 2003 Knoxville Marriott
More informationBUILDING A GRIDDED CLIMATOLOGICAL DATASET FOR USE IN THE STATISTICAL INTERPRETATION OF NUMERICAL WEATHER PREDICTION MODELS
JP 1.6 BUILDING A GRIDDED CLIMATOLOGICAL DATASET FOR USE IN THE STATISTICAL INTERPRETATION OF NUMERICAL WEATHER PREDICTION MODELS Rachel A. Trimarco, Kari L. Sheets, and Kathryn K. Hughes Meteorological
More informationan accessible interface to marine environmental data Russell Moffitt
an accessible interface to marine environmental data Russell Moffitt The Atlas Project GOAL: To provide a single point of access to oceanographic and environmental data for use by marine resource researchers,
More informationJay Lawrimore NOAA National Climatic Data Center 9 October 2013
Jay Lawrimore NOAA National Climatic Data Center 9 October 2013 Daily data GHCN-Daily as the GSN Archive Monthly data GHCN-Monthly and CLIMAT messages International Surface Temperature Initiative Global
More informationLondon Heathrow Field Site Metadata
London Heathrow Field Site Metadata Field Site Information Name: Heathrow src_id (Station ID number): 708 Geographic Area: Greater London Latitude (decimal ): 51.479 Longitude (decimal ): -0.449 OS Grid
More informationGridded monthly temperature fields for Croatia for the period
Gridded monthly temperature fields for Croatia for the 1981 2010 period comparison with the similar global and European products Melita Perčec Tadid melita.percec.tadic@cirus.dhz.hr Meteorological and
More informationA CUDA Solver for Helmholtz Equation
Journal of Computational Information Systems 11: 24 (2015) 7805 7812 Available at http://www.jofcis.com A CUDA Solver for Helmholtz Equation Mingming REN 1,2,, Xiaoguang LIU 1,2, Gang WANG 1,2 1 College
More informationApplication and verification of ECMWF products 2012
Application and verification of ECMWF products 2012 Met Eireann, Glasnevin Hill, Dublin 9, Ireland. J.Hamilton 1. Summary of major highlights The verification of ECMWF products has continued as in previous
More informationPerformance Evaluation of the Matlab PCT for Parallel Implementations of Nonnegative Tensor Factorization
Performance Evaluation of the Matlab PCT for Parallel Implementations of Nonnegative Tensor Factorization Tabitha Samuel, Master s Candidate Dr. Michael W. Berry, Major Professor Abstract: Increasingly
More informationCombining Deterministic and Probabilistic Methods to Produce Gridded Climatologies
Combining Deterministic and Probabilistic Methods to Produce Gridded Climatologies Michael Squires Alan McNab National Climatic Data Center (NCDC - NOAA) Asheville, NC Abstract There are nearly 8,000 sites
More informationSymbology Modification for Climate Studies. Rich Baldwin (NCDC/NOAA) Glen Reid (IMSG)
Symbology Modification for Climate Studies Rich Baldwin (NCDC/NOAA) Glen Reid (IMSG) June 3, 2005 Abstract The National Climatic Data Center archives worldwide weather data from the present date back to
More informationParallel Multivariate SpatioTemporal Clustering of. Large Ecological Datasets on Hybrid Supercomputers
Parallel Multivariate SpatioTemporal Clustering of Large Ecological Datasets on Hybrid Supercomputers Sarat Sreepathi1, Jitendra Kumar1, Richard T. Mills2, Forrest M. Hoffman1, Vamsi Sripathi3, William
More informationEvaluating Physical, Chemical, and Biological Impacts from the Savannah Harbor Expansion Project Cooperative Agreement Number W912HZ
Evaluating Physical, Chemical, and Biological Impacts from the Savannah Harbor Expansion Project Cooperative Agreement Number W912HZ-13-2-0013 Annual Report FY 2018 Submitted by Sergio Bernardes and Marguerite
More informationarxiv: v1 [hep-lat] 7 Oct 2010
arxiv:.486v [hep-lat] 7 Oct 2 Nuno Cardoso CFTP, Instituto Superior Técnico E-mail: nunocardoso@cftp.ist.utl.pt Pedro Bicudo CFTP, Instituto Superior Técnico E-mail: bicudo@ist.utl.pt We discuss the CUDA
More informationSome details about the theoretical background of CarpatClim DanubeClim gridded databases and their practical consequences
Some details about the theoretical background of CarpatClim DanubeClim gridded databases and their practical consequences Zita Bihari, Tamás Szentimrey, Andrea Kircsi Hungarian Meteorological Service Outline
More informationVisualizing Big Data on Maps: Emerging Tools and Techniques. Ilir Bejleri, Sanjay Ranka
Visualizing Big Data on Maps: Emerging Tools and Techniques Ilir Bejleri, Sanjay Ranka Topics Web GIS Visualization Big Data GIS Performance Maps in Data Visualization Platforms Next: Web GIS Visualization
More informationThe CLIMGEN Model. More details can be found at and in Mitchell et al. (2004).
Provided by Tim Osborn Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich NR4 7TJ, UK t.osborn@uea.ac.uk The CLIMGEN Model CLIMGEN currently produces 8 climate variables
More informationIntegrated Electricity Demand and Price Forecasting
Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form
More informationTime Series Analysis with SAR & Optical Satellite Data
Time Series Analysis with SAR & Optical Satellite Data Thomas Bahr ESRI European User Conference Thursday October 2015 harris.com Motivation Changes in land surface characteristics mirror a multitude of
More informationERBE Geographic Scene and Monthly Snow Data
NASA Contractor Report 4773 ERBE Geographic Scene and Monthly Snow Data Lisa H. Coleman, Beth T. Flug, Shalini Gupta, Edward A. Kizer, and John L. Robbins Science Applications International Corporation
More informationResearch on GPU-accelerated algorithm in 3D finite difference neutron diffusion calculation method
NUCLEAR SCIENCE AND TECHNIQUES 25, 0501 (14) Research on GPU-accelerated algorithm in 3D finite difference neutron diffusion calculation method XU Qi ( 徐琪 ), 1, YU Gang-Lin ( 余纲林 ), 1 WANG Kan ( 王侃 ),
More informationTHE METEOROLOGICAL DATA QUALITY MANAGEMENT OF THE ROMANIAN NATIONAL SURFACE OBSERVATION NETWORK
THE METEOROLOGICAL DATA QUALITY MANAGEMENT OF THE ROMANIAN NATIONAL SURFACE OBSERVATION NETWORK Ioan Ralita, Ancuta Manea, Doina Banciu National Meteorological Administration, Romania Ionel Dragomirescu
More informationNOAA s Climate Normals. Pre-release Webcast presented by NOAA s National Climatic Data Center June 13, 2011
NOAA s 1981-2010 Climate Normals Pre-release Webcast presented by NOAA s National Climatic Data Center June 13, 2011 Takeaway Messages Most Normals will be available July 1 via FTP NWS Normals to be loaded
More informationA Quantum Chemistry Domain-Specific Language for Heterogeneous Clusters
A Quantum Chemistry Domain-Specific Language for Heterogeneous Clusters ANTONINO TUMEO, ORESTE VILLA Collaborators: Karol Kowalski, Sriram Krishnamoorthy, Wenjing Ma, Simone Secchi May 15, 2012 1 Outline!
More informationMxVision WeatherSentry Web Services Content Guide
MxVision WeatherSentry Web Services Content Guide July 2014 DTN 11400 Rupp Drive Minneapolis, MN 55337 00.1.952.890.0609 This document and the software it describes are copyrighted with all rights reserved.
More informationCustomizable Drought Climate Service for supporting different end users needs
1 Customizable Drought Climate Service for supporting different end users needs Ramona MAGNO, T. De Filippis, E. Di Giuseppe, M. Pasqui, E. Rapisardi, L. Rocchi (IBIMET-CNR; LaMMA Consortium) 1 Congresso
More informationBig Data and Geospatial Cyberinfrastructure for Advancing Applications
Big Data and Geospatial Cyberinfrastructure for Advancing Applications Presented at GIScience 2012 Big Data and CyberGIS Panel Budhendra Bhaduri September 20, 2012 Columbus, OH Geospatial Cyberinfrastructure
More informationCondensing Massive Satellite Datasets For Rapid Interactive Analysis
Condensing Massive Satellite Datasets For Rapid Interactive Analysis Glenn Grant University of Colorado, Boulder With: David Gallaher 1,2, Qin Lv 1, G. Campbell 2, Cathy Fowler 2, Qi Liu 1, Chao Chen 1,
More informationAcceleration of WRF on the GPU
Acceleration of WRF on the GPU Daniel Abdi, Sam Elliott, Iman Gohari Don Berchoff, Gene Pache, John Manobianco TempoQuest 1434 Spruce Street Boulder, CO 80302 720 726 9032 TempoQuest.com THE WORLD S FASTEST
More informationOne Optimized I/O Configuration per HPC Application
One Optimized I/O Configuration per HPC Application Leveraging I/O Configurability of Amazon EC2 Cloud Mingliang Liu, Jidong Zhai, Yan Zhai Tsinghua University Xiaosong Ma North Carolina State University
More informationGIS-BASED DISASTER WARNING SYSTEM OF LOW TEMPERATURE AND SPARE SUNLIGHT IN GREENHOUSE
GIS-BASED DISASTER WARNING SYSTEM OF LOW TEMPERATURE AND SPARE SUNLIGHT IN GREENHOUSE 1,2,* 1,2 Ruijiang Wei, Chunqiang Li, Xin Wang 1, 2 1 Hebei Provincial Institute of Meteorology, Shijiazhuang, Hebei
More informationPredicting Future CO2 Amounts and Monitoring Seasonal CO2 Fluctuations QUANTIFYING CO2 ANNUAL INCREASE
http://visibleearth.nasa.gov/view.php?id=5772 3 Predicting Future CO2 Amounts and Monitoring Seasonal CO2 Fluctuations QUANTIFYING CO2 ANNUAL INCREASE Aguilera, Kimberly GEO 3276: GIS & GPS Applications
More informationVALIDATION OF SPATIAL INTERPOLATION TECHNIQUES IN GIS
VALIDATION OF SPATIAL INTERPOLATION TECHNIQUES IN GIS V.P.I.S. Wijeratne and L.Manawadu University of Colombo (UOC), Kumarathunga Munidasa Mawatha, Colombo 03, wijeratnesandamali@yahoo.com and lasan@geo.cmb.ac.lk
More informationGridded Ambient Air Pollutant Concentrations for Southern California, User Notes authored by Beau MacDonald, 11/28/2017
Gridded Ambient Air Pollutant Concentrations for Southern California, 1995-2014 User Notes authored by Beau, 11/28/2017 METADATA: Each raster file contains data for one pollutant (NO2, O3, PM2.5, and PM10)
More informationNASA Products to Enhance Energy Utility Load Forecasting
NASA Products to Enhance Energy Utility Load Forecasting Erica Zell, Battelle zelle@battelle.org, Arlington, VA ESIP 2010 Summer Meeting, Knoxville, TN, July 20-23 Project Overview Funded by the NASA Applied
More information8-km Historical Datasets for FPA
Program for Climate, Ecosystem and Fire Applications 8-km Historical Datasets for FPA Project Report John T. Abatzoglou Timothy J. Brown Division of Atmospheric Sciences. CEFA Report 09-04 June 2009 8-km
More informationUsing Of Gis Software For Mapping The Climatic Data Obtaining By Internet Network
Using Of Gis Software For Mapping The Climatic Data Obtaining By Internet Network Sabah Hussein Ali Remote Sensing Center, University of Mosul Keyword: Precipitation, GIS, Kriging interpolation, GPCC,
More informationNEAR REAL TIME GLOBAL RADIATION AND METEOROLOGY WEB SERVICES AVAILABLE FROM NASA
NEARREAL TIMEGLOBALRADIATIONANDMETEOROLOGYWEBSERVICESAVAILABLE FROMNASA ABSTRACT WilliamS.Chandler JamesM.Hoell DavidWestberg CharlesH.Whitlock TaipingZhang ScienceSystems&Applications,Inc. OneEnterpriseParkway,Suite200
More informationDisplay and analysis of weather data from NCDC using ArcGIS
Display and analysis of weather data from NCDC using ArcGIS Helen M. Cox Associate Professor Geography Department California State University, Northridge and Stephen Krug Graduate Student Geography Department
More informationScaling the Software and Advancing the Science of Global Modeling and Assimilation Systems at NASA. Bill Putman
Global Modeling and Assimilation Office Scaling the Software and Advancing the Science of Global Modeling and Assimilation Systems at NASA Bill Putman Max Suarez, Lawrence Takacs, Atanas Trayanov and Hamid
More information5B.1 DEVELOPING A REFERENCE CROP EVAPOTRANSPIRATION CLIMATOLOGY FOR THE SOUTHEASTERN UNITED STATES USING THE FAO PENMAN-MONTEITH ESTIMATION TECHNIQUE
DEVELOPING A REFERENCE CROP EVAPOTRANSPIRATION CLIMATOLOGY FOR THE SOUTHEASTERN UNITED STATES USING THE FAO PENMAN-MONTEITH ESTIMATION TECHNIQUE Heather A. Dinon*, Ryan P. Boyles, and Gail G. Wilkerson
More informationSPECIAL PROJECT PROGRESS REPORT
SPECIAL PROJECT PROGRESS REPORT Reporting year 2015 Project Title: Potential sea-ice predictability with a high resolution Arctic sea ice-ocean model Computer Project Account: Principal Investigator(s):
More informationPurdue University Meteorological Tool (PUMET)
Purdue University Meteorological Tool (PUMET) Date: 10/25/2017 Purdue University Meteorological Tool (PUMET) allows users to download and visualize a variety of global meteorological databases, such as
More informationHellenic National Meteorological Service (HNMS) GREECE
WWW TECHNICAL PROGRESS REPORT ON THE GLOBAL DATA- PROCESSING AND FORECASTING SYSTEM (GDPFS), AND THE ANNUAL NUMERICAL WEATHER PREDICTION (NWP) PROGRESS REPORT FOR THE YEAR 2005 Hellenic National Meteorological
More informationArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Sam Williamson
ArcGIS Enterprise: What s New Philip Heede Shannon Kalisky Melanie Summers Sam Williamson ArcGIS Enterprise is the new name for ArcGIS for Server What is ArcGIS Enterprise ArcGIS Enterprise is powerful
More informationUsing the EartH2Observe data portal to analyse drought indicators. Lesson 4: Using Python Notebook to access and process data
Using the EartH2Observe data portal to analyse drought indicators Lesson 4: Using Python Notebook to access and process data Preface In this fourth lesson you will again work with the Water Cycle Integrator
More informationGeospatial Tactical Decision Aids
Geospatial Tactical Decision Aids Miguel Pacheco Abstract Environmental conditions are known to affect the efficiency and effectiveness of military platforms, weapon systems and personnel. The translation
More informationPopular Mechanics, 1954
Introduction to GIS Popular Mechanics, 1954 1986 $2,599 1 MB of RAM 2017, $750, 128 GB memory, 2 GB of RAM Computing power has increased exponentially over the past 30 years, Allowing the existence of
More informationSuperPack North America
SuperPack North America Speedwell SuperPack makes available an unprecedented range of quality historical weather data, and weather data feeds for a single annual fee. SuperPack dramatically simplifies
More informationGPU Acceleration of Cutoff Pair Potentials for Molecular Modeling Applications
GPU Acceleration of Cutoff Pair Potentials for Molecular Modeling Applications Christopher Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, Wen-Mei W. Hwu University of Illinois at Urbana-Champaign
More informationhttp://www.wrcc.dri.edu/csc/scenic/ USER GUIDE 2017 Introduction... 2 Overview Data... 3 Overview Analysis Tools... 4 Overview Monitoring Tools... 4 SCENIC structure and layout... 5... 5 Detailed Descriptions
More informationAdvanced Computing Systems for Scientific Research
Undergraduate Review Volume 10 Article 13 2014 Advanced Computing Systems for Scientific Research Jared Buckley Jason Covert Talia Martin Recommended Citation Buckley, Jared; Covert, Jason; and Martin,
More informationP1.34 MULTISEASONALVALIDATION OF GOES-BASED INSOLATION ESTIMATES. Jason A. Otkin*, Martha C. Anderson*, and John R. Mecikalski #
P1.34 MULTISEASONALVALIDATION OF GOES-BASED INSOLATION ESTIMATES Jason A. Otkin*, Martha C. Anderson*, and John R. Mecikalski # *Cooperative Institute for Meteorological Satellite Studies, University of
More informationCloud-based WRF Downscaling Simulations at Scale using Community Reanalysis and Climate Datasets
Cloud-based WRF Downscaling Simulations at Scale using Community Reanalysis and Climate Datasets Luke Madaus -- 26 June 2018 luke.madaus@jupiterintel.com 2018 Unidata Users Workshop Outline What is Jupiter?
More informationCRYPTOGRAPHIC COMPUTING
CRYPTOGRAPHIC COMPUTING ON GPU Chen Mou Cheng Dept. Electrical Engineering g National Taiwan University January 16, 2009 COLLABORATORS Daniel Bernstein, UIC, USA Tien Ren Chen, Army Tanja Lange, TU Eindhoven,
More informationUNST 232 Mentor Section Assignment 5 Historical Climate Data
UNST 232 Mentor Section Assignment 5 Historical Climate Data 1 introduction Informally, we can define climate as the typical weather experienced in a particular region. More rigorously, it is the statistical
More informationThe Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea. Brief History of Weather Center. Weather Hazard Mitigation
The Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea Brief History of Weather Center Memorandum of understanding between UH Meteorology & IfA established the Mauna Kea Weather
More informationClimate Variables for Energy: WP2
Climate Variables for Energy: WP2 Phil Jones CRU, UEA, Norwich, UK Within ECEM, WP2 provides climate data for numerous variables to feed into WP3, where ESCIIs will be used to produce energy-relevant series
More informationAn Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets
IEEE Big Data 2015 Big Data in Geosciences Workshop An Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets Fatih Akdag and Christoph F. Eick Department of Computer
More informationSoftware requirements * : Part III: 2 hrs.
Title: Product Type: Developer: Target audience: Format: Software requirements * : Data: Estimated time to complete: Mapping snow cover using MODIS Part I: The MODIS Instrument Part II: Normalized Difference
More informationHarvard Center for Geographic Analysis Geospatial on the MOC
2017 Massachusetts Open Cloud Workshop Boston University Harvard Center for Geographic Analysis Geospatial on the MOC Ben Lewis Harvard Center for Geographic Analysis Aaron Williams MapD Small Team Supporting
More informationWeather Research and Forecasting (WRF) Performance Benchmark and Profiling. July 2012
Weather Research and Forecasting (WRF) Performance Benchmark and Profiling July 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell,
More informationAmundsen-Scott South Pole Station CLIMAT Message Project
Amundsen-Scott South Pole Station CLIMAT Message Project With support from the U.S. Global Climate Observing System program based at NOAA's National Climatic Data Center April 2009 Matthew A. Lazzara Linda
More informationMonitoring of grass cutting with Sentinel-1 time series
Monitoring of grass cutting with Sentinel-1 time series pilot results and vision for operational service based on big data tools and cloud computing Kaupo Voormansik 21st MARS Conference November 25, 2015
More informationCLIMATE CHANGE DATA PROJECTIONS FOR ONTARIO AND THE GREAT LAKES BASIN
CLIMATE CHANGE DATA PROJECTIONS FOR ONTARIO AND THE GREAT LAKES BASIN ECO Climate Data Roundtable, January 8, 2014 Richard Peltier, Physics, U Toronto Regional Data Sets of Climate Change Projections 2
More informationStudy on Data Integration and Sharing Standard and Specification System for Earth System Science
Study on Data Integration and Sharing Standard and Specification System for Earth System Science Juanle Wang and Jiulin Sun Information Sharing Center for Earth System Science Institute of Geographic Sciences
More informationThe general procedure for estimating 24-hour PMP includes the following steps:
14th Conference on Applied Climatology Abstract # 71520 Using PRISM Climate Grids and GIS for Extreme Precipitation Mapping George H. Taylor and Christopher Daly Oregon State University Corvallis Oregon
More information1.6 TRENDS AND VARIABILITY OF SNOWFALL AND SNOW COVER ACROSS NORTH AMERICA AND EURASIA. PART 2: WHAT THE DATA SAY
1.6 TRENDS AND VARIABILITY OF SNOWFALL AND SNOW COVER ACROSS NORTH AMERICA AND EURASIA. PART 2: WHAT THE DATA SAY David A. Robinson* Rutgers University, Department of Geography, Piscataway, New Jersey
More informationMultiphase Flow Simulations in Inclined Tubes with Lattice Boltzmann Method on GPU
Multiphase Flow Simulations in Inclined Tubes with Lattice Boltzmann Method on GPU Khramtsov D.P., Nekrasov D.A., Pokusaev B.G. Department of Thermodynamics, Thermal Engineering and Energy Saving Technologies,
More informationThe Climate of Marshall County
The Climate of Marshall County Marshall County is part of the Crosstimbers. This region is a transition region from the Central Great Plains to the more irregular terrain of southeastern Oklahoma. Average
More informationAN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS
AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS James Hall JHTech PO Box 877 Divide, CO 80814 Email: jameshall@jhtech.com Jeffrey Hall JHTech
More information8.1 CHANGES IN CHARACTERISTICS OF UNITED STATES SNOWFALL OVER THE LAST HALF OF THE TWENTIETH CENTURY
8.1 CHANGES IN CHARACTERISTICS OF UNITED STATES SNOWFALL OVER THE LAST HALF OF THE TWENTIETH CENTURY Daria Scott Dept. of Earth and Atmospheric Sciences St. Could State University, St. Cloud, MN Dale Kaiser*
More informationKEY WORDS: Palmer Meteorological Drought Index, SWAP, Kriging spatial analysis and Digital Map.
PALMER METEOROLOGICAL DROUGHT CLASSIFICATION USING TECHNIQUES OF GEOGRAPHIC INFORMATION SYSTEM IN THAILAND S. Baimoung, W. Waranuchit, S. Prakanrat, P. Amatayakul, N. Sukhanthamat, A. Yuthaphan, A. Pyomjamsri,
More informationSimulating Future Climate Change Using A Global Climate Model
Simulating Future Climate Change Using A Global Climate Model Introduction: (EzGCM: Web-based Version) The objective of this abridged EzGCM exercise is for you to become familiar with the steps involved
More informationS0214 : GPU Based Stacking Sequence Generation For Composite Skins Using GA
S0214 : GPU Based Stacking Sequence Generation For Composite Skins Using GA Date: 16th May 2012 Wed, 3pm to 3.25pm(Adv. Session) Sathyanarayana K., Manish Banga, and Ravi Kumar G. V. V. Engineering Services,
More informationDynamic Scheduling for Work Agglomeration on Heterogeneous Clusters
Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters Jonathan Lifflander, G. Carl Evans, Anshu Arya, Laxmikant Kale University of Illinois Urbana-Champaign May 25, 2012 Work is overdecomposed
More informationMapping Global Carbon Dioxide Concentrations Using AIRS
Title: Mapping Global Carbon Dioxide Concentrations Using AIRS Product Type: Curriculum Developer: Helen Cox (Professor, Geography, California State University, Northridge): helen.m.cox@csun.edu Laura
More informationStudying Topography, Orographic Rainfall, and Ecosystems (STORE)
Introduction Studying Topography, Orographic Rainfall, and Ecosystems (STORE) Lesson: Using ArcGIS Explorer to Analyze the Connection between Topography, Tectonics, and Rainfall GIS-intensive Lesson This
More informationState of GIS at the High Performance Computing Cluster
State of GIS at the High Performance Computing Cluster Peter Löwe, Jan Thaler, Stefan Lüdtke² Centre for GeoInformation Technology (CeGIT) ² Section 5.4 (Hydrology) Computing Clusters A set of loosely
More informationArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Shreyas Shinde
ArcGIS Enterprise: What s New Philip Heede Shannon Kalisky Melanie Summers Shreyas Shinde ArcGIS Enterprise is the new name for ArcGIS for Server ArcGIS Enterprise Software Components ArcGIS Server Portal
More informationInternet Engineering Jacek Mazurkiewicz, PhD
Internet Engineering Jacek Mazurkiewicz, PhD Softcomputing Part 11: SoftComputing Used for Big Data Problems Agenda Climate Changes Prediction System Based on Weather Big Data Visualisation Natural Language
More informationGPU Acceleration of Weather Forecasting and Meteorological Satellite Data Assimilation, Processing and Applications http://www.tempoquest.com Allen Huang, Ph.D. allen@tempoquest.com CTO, Tempo Quest Inc.
More informationDeliverable: D3.6. Description of the. final version of all gridded data sets of the climatology of the Carpathian Region
Deliverable D3.6 Description of the final version of all gridded data sets of the climatology of the Carpathian Region Contract number: OJEU 2010/S 110 166082 Deliverable: D3.6 Author: Igor Antolovic,
More informationApplication and verification of ECMWF products 2016
Application and verification of ECMWF products 2016 Met Eireann, Glasnevin Hill, Dublin 9, Ireland. J.Hamilton 1. Summary of major highlights The verification of ECMWF products has continued as in previous
More informationRESEARCH METHODOLOGY
III. RESEARCH METHODOLOGY 3.1 Time and Location This research has been conducted in period March until October 2010. Location of research is over Sumatra terrain. Figure 3.1 show the area of interest of
More informationHybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS
Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS Jorge González-Domínguez*, Bertil Schmidt*, Jan C. Kässens**, Lars Wienbrandt** *Parallel and Distributed Architectures
More informationArcGIS GeoAnalytics Server: An Introduction. Sarah Ambrose and Ravi Narayanan
ArcGIS GeoAnalytics Server: An Introduction Sarah Ambrose and Ravi Narayanan Overview Introduction Demos Analysis Concepts using GeoAnalytics Server GeoAnalytics Data Sources GeoAnalytics Server Administration
More informationDesign and implementation of a new meteorology geographic information system
Design and implementation of a new meteorology geographic information system WeiJiang Zheng, Bing. Luo, Zhengguang. Hu, Zhongliang. Lv National Meteorological Center, China Meteorological Administration,
More informationFUTURE CARIBBEAN CLIMATES FROM STATISTICAL AND DYNAMICAL DOWNSCALING
FUTURE CARIBBEAN CLIMATES FROM STATISTICAL AND DYNAMICAL DOWNSCALING Arnoldo Bezanilla Morlot Center For Atmospheric Physics Institute of Meteorology, Cuba The Caribbean Community Climate Change Centre
More informationEnabling ENVI. ArcGIS for Server
Enabling ENVI throughh ArcGIS for Server 1 Imagery: A Unique and Valuable Source of Data Imagery is not just a base map, but a layer of rich information that can address problems faced by GIS users. >
More informationPerformance and Application of Observation Sensitivity to Global Forecasts on the KMA Cray XE6
Performance and Application of Observation Sensitivity to Global Forecasts on the KMA Cray XE6 Sangwon Joo, Yoonjae Kim, Hyuncheol Shin, Eunhee Lee, Eunjung Kim (Korea Meteorological Administration) Tae-Hun
More informationSOFTWARE FOR WEATHER DATABASES MANAGEMENT AND CONSTRUCTION OF REFERENCE YEARS
SOFTWARE FOR WEATHER DATABASES MANAGEMENT AND CONSTRUCTION OF REFERENCE YEARS Marco Beccali 1, Ilaria Bertini 2, Giuseppina Ciulla 1, Biagio Di Pietra 2, and Valerio Lo Brano 1 1 Department of Energy,
More informationChapter 2 Variability and Long-Term Changes in Surface Air Temperatures Over the Indian Subcontinent
Chapter 2 Variability and Long-Term Changes in Surface Air Temperatures Over the Indian Subcontinent A.K. Srivastava, D.R. Kothawale and M.N. Rajeevan 1 Introduction Surface air temperature is one of the
More informationA 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40
A 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40 Lanjun Zou 1 * a,b,c Wei Gao a,d Tongwen Wu b Xiaofeng Xu b Bingyu Du a,and James Slusser d a Sino-US Cooperative Center for Remote
More informationMet Éireann Climatological Note No. 15 Long-term rainfall averages for Ireland,
Met Éireann Climatological Note No. 15 Long-term rainfall averages for Ireland, 1981-2010 Séamus Walsh Glasnevin Hill, Dublin 9 2016 Disclaimer Although every effort has been made to ensure the accuracy
More information