Statistics for Spatial Data.

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

The Global Historical Climatology Network version 2 temperature database was released in May 1997. This century-scale dataset consists of monthly surface observations from ∼7000 stations from around the world. This archive breaks considerable new ground in the field of global climate databases. The enhancements include 1) data for additional stations to improve regional-scale analyses, particularly in previously data-sparse areas; 2) the addition of maximum–minimum temperature data to provide climate information not available in mean temperature data alone; 3) detailed assessments of data quality to increase the confidence in research results; 4) rigorous and objective homogeneity adjustments to decrease the effect of nonclimatic factors on the time series; 5) detailed metadata (e.g., population, vegetation, topography) that allow more detailed analyses to be conducted; and 6) an infrastructure for updating the archive at regular intervals so that current climatic conditions can constantly be put...

https://www.ncdc.noaa.gov/monitoring-references/docs/peterson-vose-1997.pdf

An Overview of the Global Historical Climatology Network Temperature Database

This study aims to determine trends in the long-term annual mean and monthly total precipitation series using non-parametric methods (i.e. the Mann–Kendall and Sen's T tests). The change per unit time in a time series having a linear trend was estimated by applying a simple non-parametric procedure, namely Sen's estimator of slope. Serial correlation structure in the data was accounted for determining the significance level of the results of the Mann–Kendall test. The data network used in this study, which is assumed to reflect regional hydroclimatic conditions, consists of 96 precipitation stations across Turkey. Monthly totals and annual means of the monthly totals are formed for each individual station, spanning from 1929 to 1993. In this case, a total of 13 precipitation variables at each station are subjected to trend detection analysis. In addition, regional average precipitation series are established for the same analysis purpose. The application of a trend detection framework resulted in the identification of some significant trends, especially in January, February, and September precipitations and in the annual means. A noticeable decrease in the annual mean precipitation was observed mostly in western and southern Turkey, as well as along the coasts of the Black Sea. Regional average series also displayed trends similar to those for individual stations. Copyright © 2005 John Wiley & Sons, Ltd.

/pdf/trend-analysis-in-turkish-precipitation-data-4qjjgm1xpp.pdf

Trend analysis in Turkish precipitation data

/pdf/mediterranean-water-resources-in-a-global-change-scenario-14eq62ys78.pdf

Mediterranean water resources in a global change scenario

Climate zones of Turkey are redefined by using the mathematical methodology of cluster analysis. Data from 113 climate stations for temperatures (mean, maximum and minimum) and total precipitation from 1951 to 1998 are used after standardizing with zero mean and unit variance, to confirm that all variables are weighted equally in the cluster analysis. Hierarchical cluster analysis is chosen to perform the regionalization. Five different techniques were applied initially to decide the most suitable method for the region. Stability of the clusters is also tested. It is decided that Ward’s method is the most likely to yield acceptable results in this particular case, as is often the case in climatological research. Seven different climate zones are found, as in conventional climate zones, but with considerable differences at the boundaries. Copyright  2003 Royal Meteorological Society.

Redefining the climate zones of turkey using cluster analysis

Climate change and its urban-induced bias in selected Turkish cities is studied with a quality controlled temperature and precipitation data of Turkish stations in the period of 1950–2004. These stations are classified into two groups according to their populations; S1, including rural and suburban stations and S2, including large urban stations. Moving average signals, 365-day, and their digital low pass filtered versions are produced to eliminate the short term fluctuations and examine the possible trends or anomalies in climate data. Furthermore, ‘relative difference’ signals are introduced and applied to temperature and precipitation series to observe the actual local changes in the climate data independent from large-scale effects. Mann–Kendall test statistics are calculated for maximum, minimum, mean temperature and precipitation series and plotted on maps to determine any spatial trend patterns. Signal analysis show a cool period extending from early 1960s till 1993, generally with the lowest temperature values on 1992–1993 owing to the eruption of Mount Pinatubo. A last decade significant warming trend is observed in both of the series, S1 and S2, leading to 2000–2002 temperatures to be recorded as maximums in record history. The variability of urban precipitation series is generally larger than the rural ones, suggesting that urban stations can experience more frequent and severe droughts and floods. Though not significant, an increase in the urban precipitation compared to the rural one is also found. Spatial analysis resulted in significant warming in southern and southeastern parts of the country. Particularly, minimum temperature series show significant warming in almost all of the regions indicating the effect of urbanization. Significant decreases of precipitation amounts in the western parts of Turkey, such as Aegean and Trachea regions, are found. On the other hand, some Turkish northern stations show increases in precipitation of which some are significant.

/pdf/climate-change-in-turkey-for-the-last-half-century-1bxzjbu5dy.pdf

Climate change in Turkey for the last half century

Statistical and numerical modeling tools were used to investigate the climatic effects of urbanization in Istanbul, the largest city in Turkey. Mann–Kendall trend test was applied to minimum temperature data from stations located in urban, suburban and rural areas in Istanbul to determine the existence and significance of trends, and the approximate years in which changes in the trends started. In addition, using a mesoscale atmospheric model, a sensitivity experiment was carried out to explore the atmospheric effects of urbanization in the city. Both statistical and modeling analyses indicated significant warming in the atmosphere over the urbanized areas. Mann–Kendall tests indicated statistically significant positive trends in the time series of the differences in minimum temperatures between urban and rural stations. Seasonal analyses showed that the urbanization effect on climate was most pronounced in summer. In most cases, the changes in the trends occurred in the 1970s and 1980s when the population growth rate in Istanbul increased dramatically. The model results exhibited a significant expansion of the urban heat island in Istanbul from 1951 to 2004, fairly consistent with the expansion of the city in this period. A two-cell structure for the urban heat island emerged at the reference level from the difference of the July simulations with current and past landscapes: one on the European side and the other on the Asian side of the city. The maximum reference-level temperature difference between the past and present simulations was found to be around 1 °C. The modeling experiment also indicated that the velocity of the prevailing northeasterly wind and the water vapor mixing ratio were both reduced over the city. The heating effect due to urbanization was found to penetrate about 600–800 m height in the atmosphere over the city, and the two surface heat island cells were found to combine aloft. Copyright © 2006 Royal Meteorological Society

Climatic effects of urbanization in Istanbul : a statistical and modeling analysis

Effects of urbanization on climate of İstanbul and Ankara

Temperature forecasting has been one of the most important factors considered in climate impact studies on sectors of agriculture, vegetation, water resources and tourism. The main purpose of this study is to forecast daily mean, maximum and minimum temperature time series employing three different artificial neural network (ANN) methods and provide the best-fit prediction with the observed actual data using ANN algorithms.

The geographical location considered is one of Turkey's most important areas of agricultural production, the Geyve and Sakarya basin, located in the south-east of the Marmara region (40°N and 30°E). The methods chosen in this study are: (1) feed-forward back propagation (FFBP), (2) radial basis function (RBF) and, (3) generalized regression neural network (GRNN). Additionally, predictions with a multiple linear regression (MLR) model were compared to those of the ANN methods. All three different ANN methods provide satisfactory predictions in terms of the selected performance criteria; correlation coefficient (R), root mean square error (RMSE), index of agreement (IA) and the results compared well with the conventional MLR method. Copyright © 2008 Royal Meteorological Society

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.83

Mehmet Karaca

Papers

Redefining the climate zones of turkey using cluster analysis

Climate change in Turkey for the last half century

Climatic effects of urbanization in Istanbul : a statistical and modeling analysis

Effects of urbanization on climate of İstanbul and Ankara

Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods