A Review of 21st-Century Studies

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non- linear modeling procedure

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

Emergence of global scaling behaviour in the coupled Earthatmosphere interaction

Scale invariance property in the global geometry of Earth may lead to a coupled interactive behaviour between various components of the climate system. One of the most interesting correlations exists between spatial statistics of the global topography and the temperature on Earth. Here we show that the power-law behaviour observed in the Earth topography via different approaches, resembles a scaling law in the global spatial distribution of independent atmospheric parameters. We report on observation of scaling behaviour of such variables characterized by distinct universal exponents. More specifically, we find that the spatial power-law behaviour in the fluctuations of the near surface temperature over the lands on Earth, shares the same universal exponent as of the global Earth topography, indicative of the global persistent role of the static geometry of Earth to control the steady state of a dynamical atmospheric field. Such a universal feature can pave the way to the theoretical understanding of the chaotic nature of the atmosphere coupled to the Earth’s global topography

The development of a dense urban air pollution monitoring network

The importance of air pollution monitoring networks in urban areas is well known because of their miscellaneous applications. At the beginning of the 1990s, Berlin had more than 40 particulate matter monitoring stations, whereas, by 2013, there were only 12 stations. In this study, a new and free–of–charge methodology for the densifying of the PM10 monitoring network of Berlin is presented. It endeavors to find the non–linear relationship between the hourly PM10 concentration of the still–operating PM10 monitoring stations and the shut–down stations by using the Artificial Neural Network (ANN), and, consequently, the results of the shut–down stations were simulated and re–constructed. However, input–variables selection is a pre–requisite for any ANN simulation, and hence a new fuzzy–heuristic input selection has been developed and joined to the ANN for the simulation. The hourly PM10 concentrations of the 20 shut–down stations were simulated and re–constructed. The mean error, bias and absolute error of the simulations were 27.7%, –0.03 (μg/m3), and 7.4 (μg/m3), respectively. Then, the simulated hourly PM10 concentration data were converted to a daily scale and the performance of ANN models which were developed for the simulation of the daily PM10 data were evaluated (correlation coefficient >0.94). These appropriate results imply the ability of the developed input selection technique to make the appropriate selection of the input variables, and it can be introduced as a new input variable selection for the ANN. In addition, a dense PM10 monitoring network was developed by the combination of both the re–constructed (20 stations) and the current (12 stations) stations. This dense monitoring network was applied in order to determine a reliable mean annual PM10 concentration in the different areas in Berlin in 2012

Verification of daily precipitation forecast of ECMWF over Iran

# Inhaltsverzeichnis Titel und Zusammenfassung 1 1. Einleitung und Ansatz 6 2. Geographischer Hintergrund 8 2.1. Die Topographie des Irans ...10 2.2. Klimatologie ....11 2.3. Die Herkunft des Niederschlags im Iran ....13 2.4. Effektive Faktoren für den Niederschlag im Iran ........16 2.5. Der Einfluss der Gelände- Höhe auf den Niederschlag ......18 2.6. Der Zusammenhang zwischen Stations-Höhe und Niederschlag .........19 2.7. Gebiet- Klassifikation des Niederschlags .....22 2.7.1. Niederschlagsänderung mit der Gelände-Höhe ........22 2.7.2. Gebiet-Charakteristikum .....24 2.7.2.1. Gebiet 1 - Nordwesten (Aserbeidschan) ......24 2.7.2.2. Gebiet 2 - Zagros (Westen und Südwest ..24 2.7.2.3. Gebiet 3 - Norden(Astara bis Gorgan) .....25 2.7.2.4. Gebiet 4 \- Sefidrud-Einzugsgebiet .........26 2.7.2.5. Gebiet 5 - Süd-Alborz .....27 2.7.2.6. Gebiet 6 - Ost-Zagros .............28 2.7.2.7. Gebiet 7 - Offene Landschaft von Gorgan ...............29 2.7.2.8. Gebiet 8 - Nord-Khorasan ..29 2.7.2.9. Gebiet 9 Wüsten ...30 2.7.2.10. Gebiet 10- Fars-Teil vom Zagros und Persischer Golf Küste .....31 2.7.2.11. Gebiet 11- Die Küste des Golfes von Oman .....31 3. Niederschlagsdaten und Niederschlagsstatistik 34 3.1. Beobachtungsdaten 34 3.2. EZMW-Niederschlagsprognosen ...43 4\. Das EZMW-Modell 44 4.1. Beschreibung des EZMW-Prognose-Systems im Jahr 2000 .......44 4.2. Die Modellgleichungen .....45 4.3. Zeitliche und räumliche Auflösung ....46 4.4. Physikalische Verfahren in einem deterministischen Prozesse .. ..........47 4.5. Die Modell-Orographie ...47 4.6. Die Planetarische Grenzschicht .....47 4.7. Strahlung .49 4.8. Wolken ...49 4.9. Der hydrologische Kreis ..50 5\. Interpolationsmethoden 52 5.1. Inverse Distance Method 53 5.2. The nearest station ...54 5.3. Kriging .54 5.3.1. Semivariogramme .54 5.3.2. Kriging .....55 5.3.3. Punctual Kriging ... 56 5.3.4. Universal Kriging ..........58 5.4. Upscaling . ....59 5.5. Crossvalidation . ...59 6.Verifikation 60 6.1. Verifikationsmaße für kategorische Größe .....60 6.2. Verifikationsmaße für kontinuierliche Größe .. ....74 7\. Crossvalidation 80 7.1. Inverse Distance Method ......81 7.2. Nearest Station Methode ..84 7.3. Kriging ..85 7.4. Upscaling . 86 7.5. Auswahl der besten Interpolationsmethode . .. 87 8\. Ergebnisse 90 8.1 Tägliche Verifikationsergebnisse . ......90 8.1.1 1. Dez 2001 . .91 8.1.2 2\. Dez 2001 ..91 8.1.3 3. Dez 2001 ..92 8.1.4 4. Dez. 2001 .92 8.1.5 5.-6. Dez. 2001 .....92 8.1.6 7.Dez. 2001 .. ....92 8.1.7 8.-9.-10. Dez. 2001 .93 8.1.8 11.-17. Dez. 2001 .93 8.1.9 18.-22. Dez. 2001 .. ..94 8.1.10 Zusammenfassung der Wetterlage und der Niederschlagergebnisse ........119 8.2 Monatliche Verifikationsergebnisse .........120 8.3 Saisonale Verifikationsergebnisse ........128 8.4 Quantitative Verifikationsergebnisse in Form von Zeitreihen . ... .........128 8.5 Räumliche Verteilung der Verifikationsmaßzahlen ........144 8.6 Multi- kategorische Verifikation . .....168 8.7 Ergebnisse in Abhängigkeit der beobachteten Niederschlagsmenge .. ........174 8.8 Summenstatistik .. ...177 9\. Zusammenfassung und Diskussion der Ergebnisse 188 Literatur 192 Danksagung 198 Anlage A 199 Anlage B 201 Lebenslauf 205Ziel der Arbeit ist die tägliche Niederschlagsprognose von Europäischem Zentrum für mittelfristige Wettervorhersage (EZMW, Version TL511L60) über dem Iran zu verifizieren. Um dieses Ziel zu erreichen, wurden die Niederschlagsmessungen von 2048 Niederschlags-stationen (Jahr 2001) im Iran als Beobachtungsdaten betrachtet. Die 24 stündige Prognose von EZMW von t+27 bis t+51 wurde zur Verifikation verwendet. Nach der Crossvalidation wurde zwischen verschiedenen Interpolationsmethoden The Inverse Distance Method(IDM) als die geeignete Interpolationsmethode zum Interpolieren des täglichen Niederschlags im Iran ausgewählt. Zur täglichen Verifikation wurden statistische Verfahren basierend auf kontinuierlichen und kategorischen Maßzahlen verwendet. Es wurde eine Fallstudie untersucht, die den Zeitraum von 1.-22 Dezember 2001 enthält. Das EZMW-Modell hat in diesem Zeitraum die Position des beobachteten Niederschlags nur teilweise richtig vorhergesagt. Nach der Betrachtung der Boden- sowie 500 hPa-Karten wurde gezeigt, dass die hohen Werte der True Skill Statistics (Vorhersagegüte) bei mit größerem Niederschlag verbundenen Strömungen (Vorderseite des Troges) auftraten. In einzelnem im Oktober und Dezember ist die Vorhersagegüte am besten und genauesten (TSS=0.51). Nach dem Erstellen der saisonalen Verifikationsmaßzahlen wurden Herbst und Frühling als die optimalen Jahreszeiten bestimmt, in der die prognostizierten EZMW- Niederschläge mit der Beobachtung am besten übereinstimmten. Vom 1.Januar bis 21. März weist die EZMW-Zeitreihe offensichtlich eine Phasen- verschiebung von einem Tag auf, in anderen Monaten ist keine Phasenverschiebung zu bemerken. Das Modell kann in allen Monaten die Niederschläge über dem Zagros-Gebirge und im Westen und Nordwesten des Irans, an der kaspischen Küste und im Nordosten des Irans mit einem TSS größer als 0.4 vorhersagen. Die Vorhersagegüte in den 2 großen Wüsten und an der Küste des persischen Golfes und des Golfes von Oman war sehr niedrig (TSS<0.2). Die Jahreswerte von TSS (räumliche Verteilung) zeigt, dass das Modell die Niederschlagsmenge über dem Hochland und an der Luvseite des Alborz-Gebirges besser als über Flachland vorhersagen kann (TSS>0.4). Die Ergebnisse zeigen, dass in fast allen Monaten das EZMW-Modell Die Niederschläge größer als 10 mm besser als die anderen Werte (besonders in den Wintermonaten) vorhersagen kann. Die mittlere tägliche Niederschlagsmenge über das gesamte Gebiet in der Untersuchungsperiode (Jahr 2001) zeigte 0.53 mm/Tag und das EZMW hat 0.60 mm/Tag vorhergesagt (Überschätzung). Die Güte der Prognose war 0.45 für den ganzen Iran im Jahr 2001 d. h. das EZMW-Modell kann die Niederschlagsmenge im Iran mit 45% Verbesserung vorhersagen als es mit Hilfe einer Referenzprognose (Trefferrate) möglich wäre. Der EZMW Niederschlagsprognose müsste vor allem an den Tagen, an den die Niederschlagsmenge zwischen 0.1-10 mm ist in ihrer Genauigkeit verbessert werden. Die Ergebnisse dieser Arbeit sind besonders wichtig für die Iranische Meteorologische Organisation (IMO). Grund dafür ist die tägliche Anwendung der EZMW-Niederschlagsprognose für den ganzen Iran. Da der Iran bis jetzt kein lokales Modell entwickelt hat, wird täglich die Prognosen des globalen Modells verwendet, ohne Rücksicht darauf wie genau das Modell für dieses Gebiet ist. Als zukünftiges Projekt wird im Iran ein lokales Modell auf der Grundlage des EZMW-Modells entwickelt.The aim of this work is the verification of daily precipitation from European Centre for Medium Range Weather Forecast (ECMWF) over Iran. In order to achieve this goal, the precipitation measurements were regarded from 2048 precipitation stations (year 2001) in Iran as observation data. The 24 hour forecast of ECMWF from t+27 to t+51 was used for the verification. After the Cross validation ' The inverse Distance Method (IDM) ' was selected as the suitable interpolation method for interpolating the daily precipitation in Iran. For the daily verification continuous and categorical statistics has been used. The period of 1- 22 December 2001 was considered as a case study. The ECMWF model predicted the position of the observed precipitation only partly correct in this period. Regarding surface as well as 500 hPa maps, the high values of the True Skill Statistics (TSS) associated with the large amount of precipitation (ascendance of right side of trough). Between all months in October and December is the forecast quality at the best (TSS=0.51). According to seasonal verification, autumn and spring were determined as the optimal seasons, in which the predicted ECMWF precipitation was in good agreement with the observation data. From 1.Jan to 21.Mar the EZMW time series showed obviously a phase shifting of one day, in other months was any phase shifting to be noticed. The model forecasted the precipitation over the Zagros Mountains and in the west, northwest and northeast of Iran and on the Caspian coast with a TSS larger than 0.4 in all months. The forecast quality (TSS) was very small in 2 large deserts and on the coast of Persian Gulf and the gulf of Oman (TSS < 0.2). The results showed that in all months of year 2001 the ECMWF model predicted the precipitation larger than 10 mm better than the other values (especially during the winter months). The mean daily amount of precipitation over the study area in the investigation period (year 2001) was 0.53 mm and the ECMWF predicted 0.60 mm (overestimation). The True Skill Statistics (TSS) was 0.45 i.e. the EZMW model can predict the amount of precipitation in Iran with 45% improvement as it would be possible with the help of a reference forecast (hit rate). The ECMWF precipitation forecast had not good accuracy on the days, in which the amount of precipitation was between 0.1-10 mm. The results of this work are especially important for the Iranian meteorological organization. Since no local model has been developed in Iran up to now, the global precipitation forecast of ECMWF is one of the best references to estimate the daily precipitation amounts in Iran, without any knowledge of accuracy of this model for an area like Iran.

High-resolution air temperature mapping in urban areas: A review on different modelling techniques

In this study, the importance of air temperature from different aspects (e. g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio-temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression, and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations, and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio-temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

Bimodality and regime behavior in atmosphere–ocean interactions during the recent climate change

Maximum covariance analysis (MCA) and isometric feature mapping (Isomap) are applied to investigate the spatio-temporal atmosphere–ocean interactions otherwise hidden in observational data for the period of 1979–2010. Despite an established long-term surface warming trend for the whole northern hemisphere, sea surface temperatures (SST) in the East Pacific have remained relatively constant for the period of 2001–2010. Our analysis reveals that SST anomaly probability density function of the leading two Isomap components is bimodal. We conclude that Isomap shows the existence of two distinct regimes in surface ocean temperature, resembling the break and active phases of rainfall over equatorial land areas. These regimes occurred within two separated time windows during the past three decades. Strengthening of trade winds over Pacific was coincident with the cold phase of east equatorial Pacific. This pattern was reversed during the warm phase of east equatorial Pacific. The El Niño event of 1997/1998 happened within the transition mode between these two regimes and may be a trigger for the SST changes in the Pacific. Furthermore, we suggest that Isomap, compared with MCA, provides more information about the behavior and predictability of the inter-seasonal atmosphere–ocean interactions