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WRF-based Dynamical Downscaling Over High Mountain Asia = = WRF-basiertes Dynamisches Downscaling Uber Hochasien.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	WRF-based Dynamical Downscaling Over High Mountain Asia =/
其他題名:	WRF-basiertes Dynamisches Downscaling Uber Hochasien.
其他題名:	WRF-basiertes Dynamisches Downscaling Uber Hochasien.
作者:	Wang, Xun.
面頁冊數:	1 online resource (174 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Fatalities. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29100399click for full text (PQDT)
ISBN:	9798426889811

WRF-based Dynamical Downscaling Over High Mountain Asia = = WRF-basiertes Dynamisches Downscaling Uber Hochasien.
Wang, Xun.

WRF-based Dynamical Downscaling Over High Mountain Asia =WRF-basiertes Dynamisches Downscaling Uber Hochasien.WRF-basiertes Dynamisches Downscaling Uber Hochasien. - 1 online resource (174 pages)

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Technische Universitaet Berlin (Germany), 2022.

Includes bibliographical references

High Mountain Asia (HMA) is a mountainous area including the Tibetan Plateau (TP) and surrounding mountain ranges. Due to its unique climatic and tectonic settings, the HMA region is highly vulnerable to natural hazards, such as landslides and Glacial Lake Outburst Floods (GLOFs). Under climate change, the frequency of natural hazards is expected to increase, posing further threats to society and human lives in HMA.Due to the lack of meteorological data, the triggering mechanisms of atmospherically induced natural hazards, especially landslides, are still not fully understood in HMA, which hinders the development of early-warning systems. To overcome this issue, a new atmospheric data set: the High Asia Refned analysis version 2 (HAR v2), was developed and is presented in this thesis. The HAR v2 was generated by dynamical downscaling of ERA5 reanalysis data using the Weather Research and Forecasting Model (WRF). The HAR v2 provides atmospheric data at 10 km grid spacing and hourly temporal resolution. It is currently available from 2000 to 2020 and will be extended back to 1979. Compared to the old version, the HAR v2 covers a broader area and a longer temporal range.To fnd the optimal model confguration of the HAR v2, several sensitivity experiments were conducted. Validation of the HAR v2 against in-situ stations from the Global Surface Summary of the Day (GSOD) shows that the HAR v2 fts well with observations and outperforms its forcing data ERA5. In addition, the HAR v2 and a version of the HAR v2 run with 2 km grid spacing (HAR v2 2 km) were compared with other commonly used gridded precipitation data sets (reanalysis data, satellite retrieval, and interpolated in-situ observations), over a sub-region in HMA with rugged terrain. Results indicate the added value of the HAR v2 and HAR v2 2 km since they are the only products that can reproduce orographic precipitation and capture more extreme events.One of the primary goals of the HAR v2 is to provide atmospheric data for landslide researches in Kyrgyzstan and Tajikistan, one of the landslide hot spots in the HMA region. The newly developed HAR v2 was combined with historical landslide inventories to investigate the atmospheric triggering mechanisms of landslides in this region. Results reveal the crucial role of snowmelt in landslide triggering in this region and the added value of climatic disposition derived from atmospheric triggering conditions in landslide susceptibility mapping. Furthermore, the majority of previous studies applied rainfall estimates from in-situ gauges or satellite retrievals. This study also highlights the potential of dynamical downscaling products generated by regional climate models in landslide prediction.Dynamical downscaling has already been extensively applied to understand the presentday climate and also future climate. In the last part of this thesis, the applicability of dynamical downscaling in the context of paleoclimate is demonstrated. Here, two global climate simulations for the present day and the mid-Pliocene (∼3 Ma) were dynamically downscaled to 30 km grid spacing over HMA, using the same model confguration as the HAR v2. By keeping land surface conditions the same in both downscaling experiments, this study was able to isolate the infuence of large-scale climate states and reveal its role in maintaining the Qaidam mega-lake system during the mid-Pliocene. An increase of the water balance (∆S), i.e., the change in terrestrial water storage was found, when the mid-Pliocene climate is imposed on the Qaidam Basin (QB) with its modern land surface settings. This imbalance of ∆S induced solely by the changes in large-scale climate state would lead to an increase of lake extent until a new equilibrium state is reached. The estimated equilibrium lake extent is 12%-21% of the maximum lake extent approximated from proxy data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798426889811Subjects--Topical Terms:

3681792
Fatalities.
Index Terms--Genre/Form:

542853
Electronic books.

WRF-based Dynamical Downscaling Over High Mountain Asia = = WRF-basiertes Dynamisches Downscaling Uber Hochasien.
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502 $a Thesis (Ph.D.)--Technische Universitaet Berlin (Germany), 2022.
504 $a Includes bibliographical references
520 $a High Mountain Asia (HMA) is a mountainous area including the Tibetan Plateau (TP) and surrounding mountain ranges. Due to its unique climatic and tectonic settings, the HMA region is highly vulnerable to natural hazards, such as landslides and Glacial Lake Outburst Floods (GLOFs). Under climate change, the frequency of natural hazards is expected to increase, posing further threats to society and human lives in HMA.Due to the lack of meteorological data, the triggering mechanisms of atmospherically induced natural hazards, especially landslides, are still not fully understood in HMA, which hinders the development of early-warning systems. To overcome this issue, a new atmospheric data set: the High Asia Refned analysis version 2 (HAR v2), was developed and is presented in this thesis. The HAR v2 was generated by dynamical downscaling of ERA5 reanalysis data using the Weather Research and Forecasting Model (WRF). The HAR v2 provides atmospheric data at 10 km grid spacing and hourly temporal resolution. It is currently available from 2000 to 2020 and will be extended back to 1979. Compared to the old version, the HAR v2 covers a broader area and a longer temporal range.To fnd the optimal model confguration of the HAR v2, several sensitivity experiments were conducted. Validation of the HAR v2 against in-situ stations from the Global Surface Summary of the Day (GSOD) shows that the HAR v2 fts well with observations and outperforms its forcing data ERA5. In addition, the HAR v2 and a version of the HAR v2 run with 2 km grid spacing (HAR v2 2 km) were compared with other commonly used gridded precipitation data sets (reanalysis data, satellite retrieval, and interpolated in-situ observations), over a sub-region in HMA with rugged terrain. Results indicate the added value of the HAR v2 and HAR v2 2 km since they are the only products that can reproduce orographic precipitation and capture more extreme events.One of the primary goals of the HAR v2 is to provide atmospheric data for landslide researches in Kyrgyzstan and Tajikistan, one of the landslide hot spots in the HMA region. The newly developed HAR v2 was combined with historical landslide inventories to investigate the atmospheric triggering mechanisms of landslides in this region. Results reveal the crucial role of snowmelt in landslide triggering in this region and the added value of climatic disposition derived from atmospheric triggering conditions in landslide susceptibility mapping. Furthermore, the majority of previous studies applied rainfall estimates from in-situ gauges or satellite retrievals. This study also highlights the potential of dynamical downscaling products generated by regional climate models in landslide prediction.Dynamical downscaling has already been extensively applied to understand the presentday climate and also future climate. In the last part of this thesis, the applicability of dynamical downscaling in the context of paleoclimate is demonstrated. Here, two global climate simulations for the present day and the mid-Pliocene (∼3 Ma) were dynamically downscaled to 30 km grid spacing over HMA, using the same model confguration as the HAR v2. By keeping land surface conditions the same in both downscaling experiments, this study was able to isolate the infuence of large-scale climate states and reveal its role in maintaining the Qaidam mega-lake system during the mid-Pliocene. An increase of the water balance (∆S), i.e., the change in terrestrial water storage was found, when the mid-Pliocene climate is imposed on the Qaidam Basin (QB) with its modern land surface settings. This imbalance of ∆S induced solely by the changes in large-scale climate state would lead to an increase of lake extent until a new equilibrium state is reached. The estimated equilibrium lake extent is 12%-21% of the maximum lake extent approximated from proxy data.
520 $a Hochasien (engl. High Mountain Asia, HMA) ist eine gebirgige Region, die das Tibetische Plateau (TP) und die umliegenden Gebirgsketten umfasst. Aufgrund der einzigartigen klimatischen und tektonischen Gegebenheiten ist die HMA-Region sehr anfallig fur Naturgefahren wie Erdrutsche und Gletscherseeausbruche (engl. Glacier Lake Outburst Floods, GLOFs). Im Zuge des Klimawandels wird erwartet, dass die Haufgkeit von Naturgefahren zunehmen wird, was eine weitere Bedrohung fur die Gesellschaft und Menschenleben in der HMA-Region darstellt.Aufgrund des Mangels an meteorologischen Daten sind die Auslosemechanismen atmospharisch induzierter Naturgefahren, insbesondere von Erdrutschen, in der HMA-Region noch nicht vollstandig verstanden. Dies behindert die Entwicklung von Fruhwarnsystemen. Um dieses Problem zu uberwinden, wurde ein neuer atmospharischer Datensatz, die High Asia Refned analysis Version 2 (HAR v2), entwickelt und wird in dieser Arbeit vorgestellt. Die HAR v2 wurde durch dynamisches Downscaling von ERA5-Reanalysedaten unter Verwendung des Weather Research and Forecasting Model (WRF) generiert. Die HAR v2 liefert atmospharische Daten in einem 10 km Gitterabstand und stundlicher Zeitaufosung. Der Datensatz ist derzeit von 2000 bis 2020 verfugbar und wird bis 1979 zuruck erweitert. Im Vergleich zur alten Version hat die HAR v2 eine grosere raumliche Ausdehnung und einen langeren zeitlichen Bereich.Um die optimale Modellkonfguration der HAR v2 zu fnden, wurden mehrere Sensitivitatsexperimente durchgefuhrt. Die Validierung der HAR v2 mit Beobachtungen aus der Global Surface Summary of the Day (GSOD), dass die HAR v2 mit den Beobachtungen gut ubereinstimmt und die Antriebsdaten ERA5 ubertrifft. Zusatzlich wurden die HAR v2 und eine Version der HAR v2 mit 2 km Gitterabstand (HAR v2 2 km) mit anderen haufg verwendeten gerasterten Niederschlagsdatensatzen (Reanalyse-, Satellitendaten und interpolierte In-situ-Beobachtungen) verglichen. Die Ergebnisse demonstrieren die Notwendigkeit von hoher aufgelosten HAR v2- und HAR v2 2 km-Daten fur Regionen mit komplexer Topographie, da sie die einzigen Datensatze sind, die den orographischen Niederschlag reproduzieren und mehr extreme Ereignisse erfassen konnen.Eines der Hauptziele der HAR v2 ist die Bereitstellung von atmospharischen Daten fur die Erdrutschforschung in Kirgisistan und Tadschikistan, einem der Erdrutsch-Hotspots in der HMA-Region. Die neu entwickelte HAR v2 wurde mit historischen Erdrutschinventaren kombiniert, um die atmospharischen Auslosemechanismen von Erdrutschen in dieser Region zu untersuchen. Die Ergebnisse zeigen die entscheidende Rolle der Schneeschmelze bei der Auslosung von Erdrutschen in dieser Region und den zusatzlichen Wert der aus den atmospharischen Auslosebedingungen abgeleiteten klimatischen Disposition. Die meisten fruheren Studien verwendeten Niederschlagsschatzungen aus In-situ-Messgeraten oder Satellitenabruf. Diese Studie unterstreicht auch das Potenzial von dynamischen DownscalingProdukten, die von regionalen Klimamodellen erzeugt werden, fur die Vorhersage von Erdrutschen.Dynamisches Downscaling wurde bereits ausgiebig angewandt, um das heutige Klima und auch das zukunftige Klima zu verstehen. Im letzten Teil dieser Arbeit wird die Anwendbarkeit des dynamischen Downscaling im Kontext des Palaoklimas demonstriert. Hier wurden zwei globale Klimasimulationen fur die Gegenwart und das mittlere Pliozan (∼3 Ma) auf 30 km Gitterabstand verfeinert, wobei dieselbe Modellkonfguration wie bei der HAR v2 verwendet wurde. Indem die Landoberfachenbedingungen in beiden Downscaling-Experimenten gleich gehalten wurden, war diese Studie in der Lage, den Einfuss grosraumiger Klimazustande zu isolieren und ihre Rolle bei der Aufrechterhaltung des Qaidam-Megaseesystems wahrend des mittleren Pliozans aufzuzeigen. Es wurde eine Zunahme der Wasserbilanz (∆S), d.h. der Veranderung der terrestrischen Wasserspeicherung, gefunden, wenn das mittelpliozane Klima dem Qaidam-Becken (QB) mit seinen modernen Landoberfacheneinstellungen aufgezwungen wird. Dieses Ungleichgewicht von ∆S, das allein durch die Anderungen des grosraumigen Klimazustands induziert wird, wurde zu einer Zunahme der Seeausdehnung fuhren, bis ein neuer Gleichgewichtszustand erreicht ist. Die geschatzte Gleichgewichts-Seeausdehnung betragt 12%-21% der maximalen Seeausdehnung, die aus Proxydaten approximiert wurde.
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538 $a Mode of access: World Wide Web
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650 4 $a Precipitation. $3 3680799
650 4 $a Landslides & mudslides. $3 3689257
650 4 $a Wind. $3 3681788
650 4 $a Lakes. $3 546147
650 4 $a Glaciers. $3 550829
650 4 $a Topography. $3 3561794
650 4 $a Seasons. $3 523975
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650 4 $a Data assimilation. $3 3682570
650 4 $a Mountains. $3 544612
650 4 $a Time series. $3 3561811
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650 4 $a General circulation models. $3 3694484
650 4 $a Arid zones. $3 3694511
650 4 $a Rain. $3 3560372
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Geography. $3 524010
650 4 $a Geomorphology. $3 542703
650 4 $a Limnology. $3 545788
650 4 $a Physical geography. $3 516662
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