Satellite Based Communication between Land Surface Temperature and Biophysical Variables in the Jazmourian Catchment

Document Type : Research Article

Authors

1 PhD student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

2 Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

3 Assistant Professor, Department of Geography, Faculty of Humanities, University of Zanjan, Zanjan, Iran

4 PhD of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

In this research, a deterministic forecast of 24, 48 and 72 hours of 10-meter wind speed has been produced over Iran, using BMA and EMOS methods for post-processing of raw output of ensemble systems. The main purpose of this article is to compare the deterministic forecasts obtained by using these two methods with each raw ensemble members and the mean of the raw ensemble members. The used ensemble system consists of eight different physical configurations, with changes in the boundary layer scheme of the WRF model. Other physical models in ensemble system are the same for all ensemble members. Each ensemble member includes 24, 48 and 72-hour forecasts of 10-meter wind speed with a resolution of 21 kilometers over Iran. GFS forecasts are used for the initial and boundary conditions, and the forecast start time is 12 UTC per day. Observation data of 31 synoptic meteorological stations located in the provincial capitals have been used and the corresponding values of the predictions on these stations have been interpolated by bilinear method. The model is run from 1 March to 31 August 2017, and the results from 11 April to 31 August 2017 are considered as the test period. After calculating the forecast errors with different training periods, 30 days are considered as the length of training period for prediction in both BMA and EMOS methods. Verification was performed by different methods (accuracy: PC, TS and OR; reliability and resolution: FAR, POFD and POD; skill: CSS, HSS, PSS, GSS and Q; statistical errors: RMSE and MAE) for 10-meter wind speed thresholds less than 3 and more than 5, 10 and 15 m/s for both methods in all forecast ages. The results show a 3 times improvement in accuracy scores, 2.2 times improvement in reliability and resolution scores, 3.4 times improvement in skill scores and 24% reduction in statistical error scores relative to the mean of ensemble members. Furthermore, the verification results for different climatic regions (cold, semi-arid, hot-dry, hot-humid and moderate-rainy climate) in the country separately showed that in all climates, RMSE measurement has the best performance for BMA and EMOS methods and reduces the error by 21% and 23% ,respectively. In hot and humid climates, compared to the mean of ensemble members errors, these two methods were more powerful to improve the prediction system. They reduced the error by 44% and 46%, respectively.
 

Keywords

Main Subjects


ارجمند، م.، راشکی، ع.، سرگزی، ح.، 1395، پایش زمانی و مکانی پدیده گرد و غبار با استفاده از داده‌های ماهواره‌ای در جنوب شرق ایران با تأکید بر منطقه جازموریان: فصلنامه علمی- پژوهشی اطلاعات جغرافیایی، 27(106)، 153- 168.
لطفی نسب اصل، س.، خسروشاهی، م.، سعیدی فر، ز.، درگاهیان، ف.، 1397، تحلیل روند تغییرات بارندگی و ارزیابی خشکسالی­های حوضه آبخیز جازموریان با استفاده از روش­های روندیابی و شاخص­های بهینه: فصلنامه علمی- پژوهشی تحقیقات مرتع و بیابان ایران، 25(4)، 923-943.
کاردان، ر.، عزیزی، ق.، زواررضا، پ.، محمدی، ح.، 1388، مدل­سازی تأثیر دریاچه بر مناطق مجاور (مطالعه موردی مدل­سازی اقلیمی حوضه آبخیز دریاچه جازموریان با ایجاد دریاچه مصنوعی): مجله علمی- پژوهشی علوم مهندسی آبخیزداری ایران، 3(7)، 15-22.
محمدی، ع.، 1389، رسوب­ شناسی و ژئوشیمی نهشته­های پلایای جازموریان: فصلنامه علمی- پژوهشی خشک‌بوم، 1(1)، 68-79.
مهدوی نجف آبادی، ر.، احمدی کهنعلی، ج.، 1392، بررسی ظرفیت­های اکوتوریسمی منطقه جازموریان در شرایط خشکسالی: سومین همایش ملی سلامت محیط زیست و توسعه پایدار، 30 بهمن و اول اسفندماه 1392، دانشگاه آزاد اسلامی واحد بندرعباس، 1-30.
عساکره، ح.، 1390، مبانی اقلیم­شناسی آماری: انتشارات دانشگاه زنجان.
مرادی، م.، 1395، بررسی آب‌و‌هوا­شناختی دمای سطح زمین در گستره ایران با بهره­گیری از داده­های مودیس: پایان‌نامه دکتری، دانشگاه محقق اردبیلی.
ولی، ع.، رنجبر، ا.، مکرم، م.، تاری­پناه، ف.، 1398، بررسی رابطه بین دمای سطح زمین، ویژگی­های جغرافیایی و محیطی و شاخص­های بیوفیزیکی با استفاده از تصاویر لندست: مجله سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 10(3)، 35-58. 
Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M., 2001, A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations: Journal of Geophysical Research: Atmospheres, 106(D14), 14887-14907.
Carlson, T., 2007, An overview of the"triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery: Sensors, 7(8), 1612-1629.
Chakraborty, S. D., Kant, Y., and Mitra, D., 2015, Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data: Journal of Environmental Management, 148, 143-152, doi:10.1016/j.jenvman.2013.11.034.
Crum, S. M., and Jenerette, G. D., 2017, Microclimate variation among urban land covers, the importance of vertical and horizontal structure in air and land surface temperature relationships: Journal of Applied Meteorology and Climatology, 56(9), 2531-2543.
Davis, A. Y., Jung, J., Pijanowski, B. C., and Minor, E. S., 2016, Combined vegetation volume and “greenness” affect urban air temperature: Applied Geography, 71, 106-114, doi:10.1016/ j.apgeog.2016.04.010.
Doran, J. C., Hubbe, J. M., Liljegren, J. C., Shaw, W. J., Collatz, G. J., Cook, D. R., and Hart, R. L., 1998, A technique for determining the spatial and temporal distributions of surface fluxes of heat and moisture over the Southern Great Plains cloud and radiation testbed: Journal of Geophysical Research: Atmospheres, 103(D6), 6109-6121, doi:10.1029/97JD03427.
Fang, B., and Lakshmi, V., 2014, Soil moisture at watershed scale, Remote sensing techniques: Journal of Hydrology, 516, 258-272, https://doi.org/10.1016/j.jhydrol.2013.12.008.
Feng, Y., Gao, C., Tong, X., Chen, S., Lei, Z., and Wang, J., 2019, Spatial patterns of land surface temperature and their influencing factors: a case study in Suzhou, China: Remote Sensing, 11(2), 182.
Feyisa, G. L., Dons, K., and Meilby, H., 2014, Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa: Landscape and Urban Planning, 123, 87-95, doi:10.1016/j.landurbplan.2013.12.008.
Fishbein, E. S., Lee, Y., Manning, E., Maddy, E., and McMillan, W. W., 2007, AIRS/AMSU/HSB version 5 level 2 product levels, layers and trapezoids: User Doc., Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 11 pp.
Fu, G., Shen, Z., Zhang, X., Shi, P., Zhang, Y., and Wu, J., 2011, Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature: Acta Ecologica Sinica, 31(1), 8-
 
    13, doi:10.1016/j.chnaes.2010.11.002.
Gettelman, A., Weinstock, E. M., Fetzer, E. J., Irion, F. W., Eldering, A., Richard, E. C., and Herman, R. L., 2004, Validation of Aqua satellite data in the upper troposphere and lower stratosphere with in situ aircraft instruments: Geophysical Research Letters, 31(22).
Inness, A., Ades, M., Agusti-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A. M., and Suttie, M., 2019, The CAMS reanalysis of atmospheric composition: Atmospheric Chemistry and Physics, 19(6), 3515-3556.
Jenerette, G. D., Harlan, S. L., Stefanov, W. L., and Martin, C. A., 2011, Ecosystem services and urban heat riskscape moderation: water, green spaces, and social inequality in Phoenix, USA: Ecological Applications, 21(7), 2637-2651, doi:10.1890/10-1493.1.
Jenerette, G. D., Harlan, S. L., Buyantuev, A., Stefanov, W. L., Declet-Barreto, J., Ruddell, B. L., and Li, X., 2016, Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA: Landscape Ecology, 31(4), 745-760, doi:10.1007/s10980-015-0284-3.
Jin, M., and Dickinson, R. E., 2010, Land surface skin temperature climatology: Benefitting from the strengths of satellite observations: Environmental Research Letters, 5(4), 044004.
Justice, C. O., Vermote, E., Townshend, J. R., Defries, R., Roy, D. P., Hall, D. K., and Barnsley, M. J., 1998, The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research: IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228-1249.
Karimi, A., Pahlavani, P., and Bigdeli, B., 2017, Land use analysis on land surface temperature in urban areas using a geographically weighted regression and Lanndsat 8 imagery, A case study: Tehran, Iran: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42.
Kloog, I., Nordio, F., Coull, B. A., and Schwartz, J., 2014, Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA: Remote Sensing of Environment, 150, 132-139.
Li, D., and Wang, L., 2019, Sensitivity of surface temperature to land use and land cover change induced biophysical changes: The scale issue: Geophysical Research Letters, 46(16), 9678-9689, https://doi.org/10.1029/2019GL084861.
Liao, W., Liu, X., Burakowski, E., Wang, D., Wang, L., and Li, D., 2020, Sensitivities and responses of land surface temperature to deforestation-induced biophysical changes in two global earth system models: Journal of Climate, 33(19), 8381-8399.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., and Lettenmaier, D. P., 2013, A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions: Journal of Climate, 26(23), 9384-9392, doi:10.1175/ JCLI-D-12-00508.1.
Mildrexler, D. J., Zhao, M., Cohen, W. B., Running, S. W., Song, X. P., and Jones, M. O., 2018, Thermal anomalies detect critical global land surface changes: Journal of Applied Meteorology and Climatology, 57(2), 391-411.
Milly, P.C.D., 1994, Climate, soil water storage, and average annual water balance: Water Resources Research, 30(7), 2143–2156.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I. S., and Eichmann, A., 2012, The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna: NASA Technical Report Series on Global Modeling and Data Assimilation, Greenbelt, 117, 28-45.
Moon, M., Li, D., Liao, W., Rigden, A. J., and Friedl, M. A., 2020, Modification of surface energy balance during springtime: The relative importance of biophysical and meteorological changes: Agricultural and Forest Meteorology, 284, 107905.
Novák, V., 2012, Evapotranspiration in the Soil-Plant-Atmosphere System: Springer.
Odunuga, S., and Badru, G., 2015, Landcover change, land surface temperature, surface albedo and topography in the plateau region of North-Central Nigeria: Land, 4(2), 300-324.
Oyler, J. W., Ballantyne, A., Jencso, K., Sweet, M., and Running, S. W., 2015, Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature: International Journal of Climatology, 35(9), 2258-2279, doi:10.1002/joc.4127.
Pagano, T. S., Aumann, H. H., Hagan, D., and Overoye, K., 2003, Prelaunch and in-flight radiometric calibration of the Atmospheric Infrared Sounder (AIRS): IEEE Transactions on Geoscience and Remote Sensing, 41, 265–273.
Parkinson, C. L., 2003, Aqua: an earth-observing satellite mission to examine water and other climate variables: IEEE Transactions on Geoscience and Remote Sensing, 41, 173-183.
Randles, C. A., Dasilva, A., Buchard, V., Colarco, P. R., Darmenov, A. S., Govindaraju, R. C., Smirnov, A., Ferrare, R. A., Hair, J. W., and Shinozuka, Y., 2017, The MERRA-2 Aerosol reanalysis, 1980-onward, Part I: System description and data assimilation evaluation: Journal of Climatology, 30, 6823-6850.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., and Toomey, M., 2013, Climate change, phenology, and phenological control of vegetation feedbacks to the climate system: Agricultural and Forest Meteorology, 169, 156–173, https://doi.org/10.1016/j.agrformet.2012.09.012.
Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G., Berry, J. A., Collatz, G. J., Denning, A. S., Mooney, H. A., Nobre, C. A., Sato, N., Field, C. B., and Henderson-Sellers, A., 1997, Modeling the exchanges of energy, water, and carbon between continents and the atmosphere: Science, 275, 502–509, https://doi.org/10.1126/science.275.5299.502.
Shashua-Bar, I., and Hoffman, M. E., 2000, Vegetation as a climatic component in the design of an urban street:
An empirical model for predicting the cooling effect of urban green areas with trees: Energy and Buildings, 31, 221–235, doi:10.1016/S0378-7788(99)00018-3.
Shiflett, S. A., Liang, L. L., Crum, S. M., Feyisa, G. L., Wang, J., and Jenerette, G. D., 2017, Variation in the urban vegetation, surface temperature, air temperature nexus: Science of the Total Environment, 579, 495-505, doi:10.1016/j.scitotenv.2016.11.069.
Vancutsem, C., Ceccato, P., Dinku, T., and Connor, S. J., 2010, Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa: Remote Sensing of Environment, 114(2), 449-465.
Vanderborght, J., Fetzer, T., Mosthaf, K., Smits, K. M., and Helmig, R., 2017, Heat and water transport in soils and across the soil-atmosphere interface: Theory and different model concepts: Water Resources Research, 53(2), 1057-1079.
Wan, Z., Zhang, Y., Zhang, Q., and Li, Z. L., 2004, Quality assessment and validation of the MODIS global LST: International Journal of Remote Sensing, 25, 261–274, https://doi.org/10.1080/0143116031000116417.
Wan, Z., 2014, New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product: Remote Sensing of Environment, 140, 36-45.
Wu, W. S., Purser, R. J., and Parrish, D. F., 2002, Three-dimensional variational analysis with spatially inhomogeneous covariances: Monthly Weather Review, 130: 2905-2916.
Yang, F., Lau, S. S., and Qian, F., 2011, Urban design to lower summertime outdoor temperatures: An empirical study on high-rise housing in Shanghai: Building and Environment, 46(3), 769-785, doi:10.1016/j.buildenv.2010.10.010.
Zaharaddeen, I., Baba, I. I., and Zachariah, A., 2016, Estimation of land surface temperature of Kaduna Metropolis, Nigeria using Landsat images: Science World Journal, 11(3), 36-42.
Zhu, W., Lu, A., and Jia, S., 2013, Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products: Remote Sensing of Environment, 130, 62-73