Semi Operational Prediction of the Dead Sea Evaporation—A Synoptic Systems Approach


The predictability of pan evaporation and air temperature in the southern part of the Dead-Sea region (Sdom) was investigated according to two approaches, prediction by mesoscale models and with the aid of synoptic classification. First, the predicted temperature, wind speed and relative humidity that directly affect the evaporation are obtained from the WRF mesoscale model predictions. Predictions according to multilinear regression equations and a Penman-Monteith approach were also validated against observations in Sdom. The WRF model predicts the temperature reasonably well. However, the wind speed and relative humidity predictions were found to be very poor. The unique approach in this paper is employing a semi-objective synoptic systems classification according to the global GFS model. Relationships were defined between the 19 Eastern Mediterranean’s (EM) synoptic systems and the Sdom evaporation, temperature, wind speed and relative humidity. A monthly evaluation was performed for each of the systems and the semi-objective prediction was verified by the semi-objective classification. Since some synoptic systems affect the evaporation and temperature similarly, the 19 synoptic systems were grouped into seven clusters, each containing systems with similar evaporation and temperature records. This method has yielded a significant improvement in the daily prediction of evaporation and temperature. Semi-objective definitions for the synoptic systems were performed for the ranges of 12 - 132 hours. The synoptic system approach succeeded in the prediction of the evaporation and temperature changes in Sdom for a few days in advance. The predictability skill for the 12 hour forecast achieved about 80% of success, dropping to 70% at 36 hours. For 60 to 132 hours the prediction stabilized at a skill of 60%.The method presented here is a new attempt to predict meteorological parameters by using a synoptic classification approach in the Dead-Sea area where even high-resolution mesoscale modeling forecasts are not very successful.

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Ilotoviz, E. , Shafir, H. , Gasch, P. and Alpert, P. (2015) Semi Operational Prediction of the Dead Sea Evaporation—A Synoptic Systems Approach. Journal of Water Resource and Protection, 7, 1058-1074. doi: 10.4236/jwarp.2015.713087.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Hodak, E., Gottlieb, A.B., Segal, T., Politi, Y., Maron, L., Sulkes, J. and David, M. (2003) Climatotherapy at the Dead Sea Is a Remittive Therapy for Psoriasis: Combined Effects on Epidermal and Immunologic Activation. Journal of the American Academy of Dermatology, 49, 451-457.
[2] Ashbel, D. (1939) The Influence of the Dead Sea on the Climate of Its Neighborhood. Quarterly Journal of the Royal Meteorological Society, 115, 185-194.
[3] Bitan, A. (1974) The Wind Regime in the North-West Section of the Dead-Sea. Archiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 22, 313-335.
[4] Bitan, A. (1977) The Influence of the Special Shape of the Dead Sea and Its Environment on the Local Wind System. Archiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 24, 283-301.
[5] Alpert, P., Shafir, H. and Issahary, D. (1997) Recent Changes in the Climate at the Dead Sea—A Preliminary Study. Climatic Change, 37, 513-537.
[6] Shafir, H. and Alpert, P. (2011) Regional and Local Climatic Effects on the Dead-Sea Evaporation. Climatic Change, 105, 455-468.
[7] Alpert, P., Cohen, A., Neumann, J. and Doron, E. (1982) A Model Simulation of the Summer Circulation from the Eastern Mediterranean Past Lake Kinneret in the Jordan Valley. Monthly Weather Review, 100, 994-1006.<0994:AMSOTS>2.0.CO;2
[8] Shafir, H., Jin, F., Lati, Y., Cohen, M. and Alpert, P. (2008) Wind Channeling by the Dead-Sea Wadis. The Open Atmospheric Sciences Journal, 2, 139-152.
[9] Alpert, P. and Eppel, A. (1985) A Proposed Index for Mesoscale Activity. Journal of Climate and Applied Meteorology, 24, 472-480.<0472:APIFMA>2.0.CO;2
[10] Stanhill, G. (1994) Changes in the Rate of Evaporation from the Dead Sea. International Journal of Climatology, 14, 465-471.
[11] Cohen, S. and Stanhill, G. (1996) Contemporary Climate Change in the Jordan Valley. Journal of Applied Meteorology, 35, 1051-1958.<1051:CCCITJ>2.0.CO;2
[12] Alpert, P., Osetinsky, I., Ziv, B. and Shafir, H. (2004) Semi-Objective Classification for Daily Synoptic Systems: Application to the Eastern Mediterranean Climate Change. International Journal of Climatology, 24, 1001-1011.
[13] Banimahd, S.A. and Zand-Parsa, S.H. (2013) Simulation of Evaporation, Coupled Liquid Water, Water Vapor and Heat Transport through the Soil Medium. Agricultural Water Management, 130, 168-177.
[14] Teng, J.D., Yasufuku, N., Liu, Q. and Liu, S.Y. (2014) Experimental Evaluation and Parameterization of Evaporation from Soil Surface. Natural Hazards, 73, 1405-1418.
[15] Martano, P. (2015) Evapotranspiration Estimates over Non-Homogeneous Mediterranean Land Cover by a Calibrated “Critical Resistance” Approach. Atmosphere, 6, 255-272.
[16] Ganor, E., Osetinsky, I., Stupp, A. and Alpert, P. (2010) Increasing Trend of African Dust, over 49 Years, in the Eastern Mediterranean. Journal of Geophysical Research, 115, Article ID: D07201.
[17] Saaroni, H., Ziv, B., Osetinsky, I. and Alpert, P. (2010) Factors Governing the Interannual Variation and the Long-Term Trend of the 850 hPa Temperature over Israel. Quarterly Journal of the Royal Meteorological Society, 136, 305-318.
[18] Ziv, B., Saaroni, H., Pargament, R. and Alpert, P. (2013) Trends in Rainfall Regime over Israel, 1975-2010, and Their Relationship to Large-Scale Variability. Regional Environmental Change, 14, 1751-1764.
[19] Saaroni, H., Halfon, N., Ziv, B., Alpert, P. and Kutiel, H. (2010) Links between the Rainfall Regime in Israel and Location and Intensity of Cyprus Lows. International Journal of Climatology, 30, 1014-1025.
[20] Yuval, D., Broday, M. and Alpert, P. (2012) Exploring the Applicability of Future Air Quality Predictions Based on Synoptic System Forecasts. Environmental Pollution, 166, 65-74.
[21] Skamarock, W.C. and Coauthors (2008) A Description of the Advanced Research WRF Version 2. NCAR Tech Note NCAR/TN-475+STR, 113 p.
[22] Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998) Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No.56, Rome, 300 p.
[23] Rimmer, A., Samuels, R. and Lechinsky, Y. (2009) A Comprehensive Study across Methods and Time Scales to Estimate Surface Fluxes from Lake Kinneret, Israel. Journal of Hydrology, 379, 181-192.
[24] Branch, O., Warrach-Sagi, K., Wulfmeyer, V. and Cohen, S. (2014) Simulation of Semi-Arid Biomass Plantations and Irrigation Using the WRF-NOAH Model—A Comparison with Observations from Israel. Hydrology and Earth System Sciences (HESS), 18, 1761-1783.
[25] Alpert, P., Osetinsky, I., Ziv, B. and Shafir, H. (2004) A New Season’s Definition Based on the Classified Daily Synoptic Systems: An Example for the Eastern Mediterranean. International Journal of Climatology, 24, 1013-1021.
[26] Alpert, P. and Ziv, B. (1989) The Sharav Cyclone—Observations and Some Theoretical Considerations. Journal of Geophysical Research, 94, 18495-18514.
[27] Hamill, T.M., Whitaker, J.S. and Mullen, S.L. (2006) Reforecasts: An Important Dataset for Improving Weather Predictions. Bulletin of the American Meteorological Society, 87, 33-46.

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