Predictability of Seasonal Precipitation Using Joint Probabilities

Download

index.pdf

Date

2010-01-17

Author

Yılmaz, Mustafa Tuğrul

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

227
views

96
downloads

This paper tests whether seasonal mean precipitation is predictable using a new method that estimates and analyzes joint probabilities. The new estimation method is to partition the globe into boxes, pool all data within the box to estimate a single joint probability of precipitation for two consecutive seasons, and then apply the resulting joint probability to individual pixels in the box. Pooling data in this way allows joint probabilities to be estimated in relatively small sample sizes, but assumes that the transition probabilities of pixels in a box are homogeneous and stationary. Joint probabilities are estimated from the Global Precipitation Climatology Project data set in 21 land boxes and 5 ocean boxes during the period 1979-2008. The state of precipitation is specified by dry, wet, or normal terciles of the local climatological distribution. Predictability is quantified by mutual information, which is a fundamental measure of predictability that allows for nonlinear dependencies, and tested using bootstrap methods. Predictability was verified by constructing probabilistic and quantitative forecasts directly from the transition probabilities and showing that they have superior cross-validated skill than forecasts based on climatology, persistence, or random selection. Spring was found to be the most predictable season whereas summer was the least predictable season. Analysis of joint probabilities reveals that though the probabilities are close to climatology, the predictability of precipitation arises from a slight tendency of the state to persist from one season to the next, or if a transition occurs then it is more often from one extreme to normal than from one extreme to the other.

Subject Keywords

Atmospheric Science

URI

https://hdl.handle.net/11511/34370

DOI

https://doi.org/10.1175/2009jhm1187.1

Collections

Department of Civil Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Nonstationarity impacts on frequency analysis of yearly and seasonal extreme temperature in Turkey Aziz, Rizwan; Yücel, İsmail; Yozgatlıgil, Ceylan (Elsevier BV, 2020-07-01) This study investigates the temporal variability in yearly and seasonal extreme temperatures across Turkey using stationary and nonstationary frequency analysis. The analyses are conducted using Generalized Extreme Value (GEV), Gumbel and Normal distributions for minimum and maximum temperatures during historical (1971-2016) and projection period (2051-2100). The future nonstationarity impacts are quantified using a 12-member ensemble of The Coordinated Regional Downscaling Experiment (CORDEX) regional clim...
Assessing nonstationarity impacts for historical and projected extreme precipitation in Turkey Aziz, Rizwan; Yücel, İsmail (Springer Science and Business Media LLC, 2021-01-01) The temporal variability in yearly and seasonal extreme precipitation across Turkey is investigated using stationary and nonstationary frequency approach. Four frequency distributions namely, generalized extreme value (GEV), gumbel, normal, and lognormal distributions are used for the historical period (1971-2016) as well as the projection period (2051-2100). The nonstationarity impacts are determined by calculating the percentage difference of return levels (30 years) between stationary and nonstationary c...
Clustering current climate regions of Turkey by using a multivariate statistical method İyigün, Cem; Batmaz, İnci; Yozgatlıgil, Ceylan; Koc, Elcin Kartal; Ozturk, Muhammed Z. (Springer Science and Business Media LLC, 2013-10-01) In this study, the hierarchical clustering technique, called Ward method, was applied for grouping common features of air temperature series, precipitation total and relative humidity series of 244 stations in Turkey. Results of clustering exhibited the impact of physical geographical features of Turkey, such as topography, orography, land-sea distribution and the high Anatolian peninsula on the geographical variability. Based on the monthly series of nine climatological observations recorded for the period...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting Yılmaz, Koray Kamil; Hsu, KL; Sorooshian, S; Gupta, HV; Wagener, T (American Meteorological Society, 2005-08-01) This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agre...
Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis Yılmaz, Mustafa Tuğrul (American Meteorological Society, 2014-06-01) Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has sp...

Citation Formats

M. T. Yılmaz, “Predictability of Seasonal Precipitation Using Joint Probabilities,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34370.