Estimating drought conditions for regions with limited precipitation data

Journal of Applied Meteorology and Climatology

Volumn 50, Issue 3, 2011, Pages 548-559

  Rhee, Jinyoung ^a   Carbone, Gregory J ^a  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DROUGHT CONDITIONS; DROUGHT INDICES; DROUGHT MONITORING; MULTI SENSOR; NATIONAL WEATHER SERVICES; PRECIPITATION DATA; RAINFALL ESTIMATES; SPATIAL INTERPOLATION; SPATIAL TEMPORALS; TROPICAL RAINFALL MEASURING MISSION SATELLITES; WEATHER STATIONS; WESTERN UNITED STATES;

DATA HANDLING; DROUGHT; ESTIMATION; RAIN; SOIL MOISTURE; SPACE SHUTTLES; WEATHER INFORMATION SERVICES;

RAIN GAGES;

CLIMATE CONDITIONS; DROUGHT; INTERPOLATION; MONITORING SYSTEM; PRECIPITATION ASSESSMENT; RAINFALL; REGIONAL CLIMATE; SENSOR; SPATIOTEMPORAL ANALYSIS;

EID: 79955038581     PISSN: 15588424     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010JAMC2604.1     Document Type: Article

References (42)

1. Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor., 4, 1147-1167.
2. Carbone, G. J., J. Rhee, H. P. Mizzell, and R. Boyles, 2008: Decision support: A regional-scale drought monitoring tool for the Carolinas. Bull. Amer. Meteor. Soc., 89, 20-28.
3. Coulibaly, P., and N. D. Evora, 2007: Comparison of neural network methods for infilling missing daily weather records. J. Hydrol., 341, 27-41.
4. Daly, C.,W. P.Gibson,G.H. Taylor, M. K. Doggett, and J. I. Smith, 2007: Observer bias in daily precipitation measurements at United States Cooperative Network stations. Bull. Amer. Meteor. Soc., 88, 899-912.
5. DeGaetano, A. T., and B. N. Belcher, 2007: Spatial interpolation of daily maximum and minimum air temperature based on meteorological model analyses and independent observations. J. Appl. Meteor. Climatol., 46, 1981-1992.
6. Dirks, K. N., J. E. Hay, C. D. Stow, and D. Harris, 1998: Highresolution studies of rainfall on Norfolk Island. Part II: Interpolation of rainfall data. J. Hydrol., 208, 187-193.
7. Eischeid, J. K., P. A. Pasteris, H. F. Diaz, M. S. Plantico, and N. J. Lott, 2000: Creating a serially complete, national daily time series of temperature and precipitation for the western United States. J. Appl. Meteor., 39, 1580-1591.
8. Guttman, N. B., 1998: Comparing the Palmer drought index and the standardized precipitation index. J. Amer. Water Resour. Assoc., 34, 113-121.
9. Guttman, N. B., 1999: Accepting the Standardized Precipitation Index: A calculation algorithm. J. Amer.Water Resour. Assoc., 35, 311-322.
10. Han, W. S., S. J. Burian, and J. M. Shepherd, 2010: Assessment of satellite-based rainfall estimates in urban areas in different geographic and climate regions. Nat. Hazards, 56, 733-747.
11. Hand, L. M., and J. M. Shepherd, 2009: An investigation of warmseason spatial rainfall variability in Oklahoma City: Possible linkages to urbanization and prevailing wind. J. Appl. Meteor. Climatol., 48, 251-269.
12. Hartkamp, A. D., K. De Beurs, A. Stein, and J. W. White, 1999: Interpolation techniques for climate variables. International Maize and Wheat Improvement Center (CIMMYT), NRGGIS Series 99-01, 34 pp.
13. Heim, R. R., Jr., 2002: A review of twentieth-century drought indices used in theUnited States.Bull.Amer.Meteor. Soc., 83, 1149-1165.
14. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38-55.
15. Im, J., 2006: A remote sensing change detection system based on neighborhood/object correlation image analysis, expert systems, and an automated calibration model. Ph.D. dissertation, University of South Carolina, 178 pp.
16. Jeffrey, S. J., J. O. Carter, K. B. Moodie, and A. R. Beswick, 2001: Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Modell. Software, 16, 309-330.
17. Jolly, W. M., and S. W. Running, 2004: Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari. Global Change Biol., 10, 303-308.
18. Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates frompassivemicrowave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487-503.
19. Kidd, C., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 1088-1104.
20. Lam, N. S.-N., 1983: Spatial interpolation methods: A review. Amer. Geogr., 10, 129-149.
21. Li, J., and A. D. Heap, 2008: A Review of Spatial Interpolation Methods for Environmental Scientists. Geoscience Australia, Record 2008/23, 137 pp.
22. McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179-184.
23. Mitas, L., and H. Mitasova, 1999: Spatial interpolation. Geographical Information Systems: Principles, Techniques, Multivariate Interpolation of PrecipitationManagement andApplications, P.A. Longley et al., Eds., John Wiley and Sons, 481-492.
24. NASDA, 2001: TRMM Data Users Handbook. National Space Development Agency of Japan, Earth Observation Center, 226 pp.
25. Palmer, W. C., 1965: Meteorological drought. U.S. Weather Bureau Research Paper 45, 58 pp.
26. Paulhus, J. L. H., and M. A. Kohler, 1952: Interpolation of missing precipitation records. Mon. Wea. Rev., 80, 129-133.
27. Quiring, S.M., 2009: Developing objective operational definitions for monitoring drought. J. Appl. Meteor. Climatol., 48, 1217-1229.
28. Rhee, J.,G. J.Carbone, and J.Hussey, 2008:Drought indexmapping at different spatial units. J. Hydrometeor., 9, 1523-1534.
29. Rhee, J., J. Im, and G. J. Carbone, 2010: Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ., 114, 2875-2887.
30. Rudolf, B., and U. Schneider, 2005: Calculation of gridded precipitation data for the global land-surface using in-situ gauge observations. Proc. Second Workshop of the Int. Precipitation Working Group, Monterey, CA, EUMETSAT, 231-247.
31. Seo, D.-J., 1998: Real-time estimation of rainfall fields using radar rainfall and rain gauge data. J. Hydrol., 208, 37-52.
32. Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
33. Tabios, G. Q., and J. D. Salas, 1985: A comparative analysis of techniques for spatial interpolation of precipitation. Water Resour. Bull., 21, 365-380.
34. Vincenty, T., 1975: Direct and inverse solutions of geodesics on the ellipsoidwith application of nested equations. Surv.Rev.XXIII, 176, 88-93.
35. Vose, R. S., R. L. Schmoyer, P. M. Steurer, T. C. Peterson, R. Heim, T. R. Karl, and J. K. Eischeid, 1992: The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data. ORNL/CDIAC-53, NDP-041, 324 pp. [Available from Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831.]
36. Wilhite, D. A., and M. Buchanan-Smith, 2005: Drought as hazard: Understanding the natural and social context. Drought and Water Crises: Science, Technology, and Management Issues, D. A. Wilhite, Ed., CRC, 3-29.
37. Williams, C. N., Jr., M. J. Menne, R. S. Vose, and D. R. Easterling, 2004: United States Historical Climatology Network daily temperature, precipitation, and snow data. ORNL/CDIAC-118, NDP-070. [Available online at http://cdiac.ornl.gov/epubs/ndp/ushcn/usa.html.]
38. Willmott, C. J., 1981: On the validation of models. Phys. Geogr., 2, 184-194.
39. Willmott, C. J., 1982: Some comments on the evaluation of model performance. Bull. Amer. Meteor. Soc., 63, 1309-1313.
40. Willmott, C. J., and K. Matsuura, 2005: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res., 30, 79-82.
41. Willmott, C. J., S. M. Robeson, and M. J. Janis, 1996: Comparison of approaches for estimating time-averaged precipitation using data from the USA. Int. J. Climatol., 16, 1103-1115.
42. WMO, 1989: Calculation of monthly and annual 30-year standard normals. WCDP 10, WMO-TD 341, 11 pp.