TY - JOUR A1 - Castino, Fabiana A1 - Bookhagen, Bodo A1 - Strecker, Manfred T1 - Rainfall variability and trends of the past six decades (1950-2014) in the subtropical NW Argentine Andes JF - Climate dynamics : observational, theoretical and computational research on the climate system N2 - The eastern flanks of the Central Andes are characterized by deep convection, exposing them to hydrometeorological extreme events, often resulting in floods and a variety of mass movements. We assessed the spatiotemporal pattern of rainfall trends and the changes in the magnitude and frequency of extreme events (ae95th percentile) along an E-W traverse across the southern Central Andes using rain-gauge and high-resolution gridded datasets (CPC-uni and TRMM 3B42 V7). We generated different climate indices and made three key observations: (1) an increase of the annual rainfall has occurred at the transition between low (< 0.5 km) and intermediate (0.5-3 km) elevations between 1950 and 2014. Also, rainfall increases during the wet season and, to a lesser degree, decreases during the dry season. Increasing trends in annual total amounts characterize the period 1979-2014 in the arid, high-elevation southern Andean Plateau, whereas trend reversals with decreasing annual total amounts were found at low elevations. (2) For all analyzed periods, we observed small or no changes in the median values of the rainfall-frequency distribution, but significant trends with intensification or attenuation in the 95th percentile. (3) In the southern Andean Plateau, extreme rainfall events exhibit trends towards increasing magnitude and, to a lesser degree, frequency during the wet season, at least since 1979. Our analysis revealed that low (< 0.5 km), intermediate (0.5-3 km), and high-elevation (> 3 km) areas respond differently to changing climate conditions, and the transition zone between low and intermediate elevations is characterized by the most significant changes. KW - Extreme rainfall KW - South American Monsoon System KW - Central Andes KW - Quantile regression KW - Rain gauges KW - CPC-uni KW - TRMM KW - Orographic barrier Y1 - 2017 U6 - https://doi.org/10.1007/s00382-016-3127-2 SN - 0930-7575 SN - 1432-0894 VL - 48 SP - 1049 EP - 1067 PB - Springer CY - New York ER - TY - GEN A1 - Ramezani Ziarani, Maryam A1 - Bookhagen, Bodo A1 - Schmidt, Torsten A1 - Wickert, Jens A1 - de la Torre, Alejandro A1 - Deng, Zhiguo A1 - Calori, Andrea T1 - A model for the relationship between rainfall, GNSS-derived integrated water vapour, and CAPE in the eastern central Andes T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1172 KW - Global Navigation Satellite System (GNSS) KW - GNSS-integrated water vapour KW - convective available potential energy (CAPE) KW - extreme rainfall KW - TRMM Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523256 SN - 1866-8372 IS - 1172 ER - TY - JOUR A1 - Ramezani Ziarani, Maryam A1 - Bookhagen, Bodo A1 - Schmidt, Torsten A1 - Wickert, Jens A1 - de la Torre, Alejandro A1 - Deng, Zhiguo A1 - Calori, Andrea T1 - A model for the relationship between rainfall, GNSS-derived integrated water vapour, and CAPE in the eastern central Andes JF - Remote Sensing N2 - Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes. KW - Global Navigation Satellite System (GNSS) KW - GNSS-integrated water vapour KW - convective available potential energy (CAPE) KW - extreme rainfall KW - TRMM Y1 - 2021 U6 - https://doi.org/10.3390/rs13183788 SN - 2072-4292 VL - 13 IS - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hierro, Rodrigo A1 - Burgos Fonseca, Y. A1 - Ramezani Ziarani, Maryam A1 - Llamedo, P. A1 - Schmidt, Torsten A1 - de la Torre, Alejandro A1 - Alexander, P. T1 - On the behavior of rainfall maxima at the eastern Andes JF - Atmospheric Research N2 - In this study, we detect high percentile rainfall events in the eastern central Andes, based on Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25 × 0.25°, a temporal resolution of 3 h, and for the duration from 2001 to 2018. We identify three areas with high mean accumulated rainfall and analyze their atmospheric behaviour and rainfall characteristics with specific focus on extreme events. Extreme events are defined by events above the 95th percentile of their daily mean accumulated rainfall. Austral summer (DJF) is the period of the year presenting the most frequent extreme events over these three regions. Daily statistics show that the spatial maxima, as well as their associated extreme events, are produced during the night. For the considered period, ERA-Interim reanalysis data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) with 0.75° x0.75° spatial and 6-hourly temporal resolutions, were used for the analysis of the meso- and synoptic-scale atmospheric patterns. Night- and day-time differences indicate a nocturnal overload of northerly and northeasterly low-level humidity flows arriving from tropical South America. Under these conditions, cooling descending air from the mountains may find unstable air at the surface, giving place to the development of strong local convection. Another possible mechanism is presented here: a forced ascent of the low-level flow due to the mountains, disrupting the atmospheric stratification and generating vertical displacement of air trajectories. A Principal Component Analysis (PCA) in T-mode is applied to day- and night-time data during the maximum and extreme events. The results show strong correlation areas over each subregion under study during night-time, whereas during day-time no defined patterns are found. This confirms the observed nocturnal behavior of rainfall within these three hotspots. KW - South-America KW - rainy-season KW - part I KW - precipitation KW - TRMM KW - climate KW - summer KW - circulation KW - monsoon KW - systems Y1 - 2019 U6 - https://doi.org/10.1016/j.atmosres.2019.104792 SN - 0169-8095 VL - 234 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - THES A1 - Crisologo, Irene T1 - Using spaceborne radar platforms to enhance the homogeneity of weather radar calibration T1 - Homogenisierung der Kalibrierung von Niederschlagsradaren mit Hilfe satellitengestützter Radarplattformen N2 - Accurate weather observations are the keystone to many quantitative applications, such as precipitation monitoring and nowcasting, hydrological modelling and forecasting, climate studies, as well as understanding precipitation-driven natural hazards (i.e. floods, landslides, debris flow). Weather radars have been an increasingly popular tool since the 1940s to provide high spatial and temporal resolution precipitation data at the mesoscale, bridging the gap between synoptic and point scale observations. Yet, many institutions still struggle to tap the potential of the large archives of reflectivity, as there is still much to understand about factors that contribute to measurement errors, one of which is calibration. Calibration represents a substantial source of uncertainty in quantitative precipitation estimation (QPE). A miscalibration of a few dBZ can easily deteriorate the accuracy of precipitation estimates by an order of magnitude. Instances where rain cells carrying torrential rains are misidentified by the radar as moderate rain could mean the difference between a timely warning and a devastating flood. Since 2012, the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) has been expanding the country’s ground radar network. We had a first look into the dataset from one of the longest running radars (the Subic radar) after devastating week-long torrential rains and thunderstorms in August 2012 caused by the annual southwestmonsoon and enhanced by the north-passing Typhoon Haikui. The analysis of the rainfall spatial distribution revealed the added value of radar-based QPE in comparison to interpolated rain gauge observations. However, when compared with local gauge measurements, severe miscalibration of the Subic radar was found. As a consequence, the radar-based QPE would have underestimated the rainfall amount by up to 60% if they had not been adjusted by rain gauge observations—a technique that is not only affected by other uncertainties, but which is also not feasible in other regions of the country with very sparse rain gauge coverage. Relative calibration techniques, or the assessment of bias from the reflectivity of two radars, has been steadily gaining popularity. Previous studies have demonstrated that reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and its successor, the Global Precipitation Measurement (GPM), are accurate enough to serve as a calibration reference for ground radars over low-to-mid-latitudes (± 35 deg for TRMM; ± 65 deg for GPM). Comparing spaceborne radars (SR) and ground radars (GR) requires cautious consideration of differences in measurement geometry and instrument specifications, as well as temporal coincidence. For this purpose, we implement a 3-D volume matching method developed by Schwaller and Morris (2011) and extended by Warren et al. (2018) to 5 years worth of observations from the Subic radar. In this method, only the volumetric intersections of the SR and GR beams are considered. Calibration bias affects reflectivity observations homogeneously across the entire radar domain. Yet, other sources of systematic measurement errors are highly heterogeneous in space, and can either enhance or balance the bias introduced by miscalibration. In order to account for such heterogeneous errors, and thus isolate the calibration bias, we assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a qualityweighted average of reflectivity differences in any sample of matching SR–GR volumes. We exemplify the idea of quality-weighted averaging by using beam blockage fraction (BBF) as a quality variable. Quality-weighted averaging is able to increase the consistency of SR and GR observations by decreasing the standard deviation of the SR–GR differences, and thus increasing the precision of the bias estimates. To extend this framework further, the SR–GR quality-weighted bias estimation is applied to the neighboring Tagaytay radar, but this time focusing on path-integrated attenuation (PIA) as the source of uncertainty. Tagaytay is a C-band radar operating at a lower wavelength and is therefore more affected by attenuation. Applying the same method used for the Subic radar, a time series of calibration bias is also established for the Tagaytay radar. Tagaytay radar sits at a higher altitude than the Subic radar and is surrounded by a gentler terrain, so beam blockage is negligible, especially in the overlapping region. Conversely, Subic radar is largely affected by beam blockage in the overlapping region, but being an SBand radar, attenuation is considered negligible. This coincidentally independent uncertainty contributions of each radar in the region of overlap provides an ideal environment to experiment with the different scenarios of quality filtering when comparing reflectivities from the two ground radars. The standard deviation of the GR–GR differences already decreases if we consider either BBF or PIA to compute the quality index and thus the weights. However, combining them multiplicatively resulted in the largest decrease in standard deviation, suggesting that taking both factors into account increases the consistency between the matched samples. The overlap between the two radars and the instances of the SR passing over the two radars at the same time allows for verification of the SR–GR quality-weighted bias estimation method. In this regard, the consistency between the two ground radars is analyzed before and after bias correction is applied. For cases when all three radars are coincident during a significant rainfall event, the correction of GR reflectivities with calibration bias estimates from SR overpasses dramatically improves the consistency between the two ground radars which have shown incoherent observations before correction. We also show that for cases where adequate SR coverage is unavailable, interpolating the calibration biases using a moving average can be used to correct the GR observations for any point in time to some extent. By using the interpolated biases to correct GR observations, we demonstrate that bias correction reduces the absolute value of the mean difference in most cases, and therefore improves the consistency between the two ground radars. This thesis demonstrates that in general, taking into account systematic sources of uncertainty that are heterogeneous in space (e.g. BBF) and time (e.g. PIA) allows for a more consistent estimation of calibration bias, a homogeneous quantity. The bias still exhibits an unexpected variability in time, which hints that there are still other sources of errors that remain unexplored. Nevertheless, the increase in consistency between SR and GR as well as between the two ground radars, suggests that considering BBF and PIA in a weighted-averaging approach is a step in the right direction. Despite the ample room for improvement, the approach that combines volume matching between radars (either SR–GR or GR–GR) and quality-weighted comparison is readily available for application or further scrutiny. As a step towards reproducibility and transparency in atmospheric science, the 3D matching procedure and the analysis workflows as well as sample data are made available in public repositories. Open-source software such as Python and wradlib are used for all radar data processing in this thesis. This approach towards open science provides both research institutions and weather services with a valuable tool that can be applied to radar calibration, from monitoring to a posteriori correction of archived data. N2 - Die zuverlässige Messung des Niederschlags ist Grundlage für eine Vielzahl quantitativer Anwendungen. Bei der Analyse und Vorhersage von Naturgefahren wie Sturzfluten oder Hangrutschungen ist dabei die räumliche Trennschärfe der Niederschlagsmessung besonders wichtig, da hier oft kleinräumige Starkniederschläge auslösend sind. Seit dem 2.Weltkrieg gewinnen Niederschlagsradare an Bedeutung für die flächenhafte Erfassung des Niederschlags in hoher raum-zeitlicher Aulösung. Und seit Ende des 20. Jahrhunderts investieren Wetterdienste zunehmend in die Archivierung dieser Beobachtungen. Die quantitative Auswertung solcher Archive gestaltet sich jedoch aufgrund unterschiedlicher Fehlerquellen als schwierig. Eine Fehlerquelle ist die Kalibrierung der Radarsysteme, die entlang der sog. "receiver chain" eine Beziehung zwischen der primären Beobachtungsvariable (der zurückgestreuten Strahlungsleistung) und der Zielvariable (des Radarreflektivitätsfaktors, kurz Reflektivität) herstellt. Die Reflektivität wiederum steht über mehrere Größenordnungen hinweg in Beziehung zur Niederschlagsintensität, so dass bereits kleine relative Fehler in der Kalibrierung große Fehler in der quantitativen Niederschlagsschätzung zur Folge haben können. Doch wie kann eine mangelhafte Kalibrierung nachträglich korrigiert werden? Diese Arbeit beantwortet diese Frage am Beispiel des kürzlich installierten Radarnetzwerks der Philippinen. In einer initialen Fallstudie nutzen wir das S-Band-Radar nahe Subic, welches die Metropolregion Manila abdeckt, zur Analyse eines außergewöhnlich ergiebigen Niederschlagsereignisses im Jahr 2012: Es zeigt sich, dass die radargestützte Niederschlagsschätzung um rund 60% unter den Messungen von Niederschlagsschreibern liegt. Kann die Hypothese einer mangelhaften Kalibrierung bestätigt werden, indem die Beobachtungen des Subic-Radars mit den Messungen exzellent kalibrierter, satellitengestützter Radarsysteme verglichen werden? Kann die satellitengestützte Referenz ggf. sogar für eine nachträgliche Kalibrierung genutzt werden? Funktioniert eine solche Methode auch für das benachbarte C-Band-Radar nahe Tagaytay? Können wir die Zuverlässigkeit einer nachträglichen Kalibrierung erhöhen, indem wir andere systematische Fehlerquellen in den Radarmessungen identifizieren? Zur Beantwortung dieser Fragen vergleicht diese Arbeit die Beobachtungen bodengestützter Niederschlagsradare (GR) mit satellitengestützten Niederschlagsradaren (SR) der Tropical Rainfall Measuring Mission (TRMM) und ihrem Nachfolger, der Global Precipitation Measurement (GPM) Mission. Dazu wird eine Methode weiterentwickelt, welche den dreidimensionalen Überlappungsbereich der Samplingvolumina des jeweiligen Instruments|GR und SR|berücksichtigt. Desweiteren wird jedem dieser Überlappungsbereiche ein Wert für die Datenqualität zugewiesen, basierend auf zwei Unsicherheitsquellen: dem Anteil der Abschattung (engl. beam blockage fraction, BBF) und der pfadintegrierten Dämpfung (engl. path-integrated attenuation, PIA). Die BBF zeigt, welcher Anteil des Radarstrahls von der Geländeoberfläche blockiert wird (je höher, desto niedriger die Qualität). PIA quantifiziert den Energieverlust des Signals, wenn es intensiven Niederschlag passiert (je höher, desto niedriger die Qualität). Entsprechend wird der Bias (also der Kalibrierungsfaktor) als das qualitätsgewichtete Mittel der Differenzen zwischen den GR- und SR-Reflektivitäten (ausgedrückt auf der logarithmischen Dezibelskala) berechnet. Diese Arbeit zeigt, dass beide Radare, Subic und Tagaytay, gerade in den frühen Jahren stark von mangelhafter Kalibrierung betroffen waren. Der Vergleich mit satellitengestützten Messungen erlaubt es uns, diesen Fehler nachträglich zu schätzen und zu korrigieren. Die Zuverlässigkeit dieser Schätzung wird durch die Berücksichtigung anderer systematischer Fehler im Rahmen der Qualitätsgewichtung deutlich erhöht. Dies konnte auch dadurch bestätigt werden, dass nach Korrektur der Kalibierung die Signale im Überlappungsbereich der beiden bodengestützten Radare deutlich konsistenter wurden. Eine Interpolation des Fehlers in der Zeit war erfolgreich, so dass die Radarbeobachtungen auch für solche Tage korrigiert werden können, an denen keine satellitengestützten Beobachtungen verfügbar sind. KW - Niederschlagsradar KW - Kalibrierung KW - Fernerkundung KW - TRMM KW - GPM KW - Philippinen KW - weather radar KW - calibration KW - remote sensing KW - TRMM KW - GPM KW - The Philippines Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-445704 ER - TY - JOUR A1 - Wulf, Hendrik A1 - Bookhagen, Bodo A1 - Scherler, Dirk T1 - Differentiating between rain, snow, and glacier contributions to river discharge in the western Himalaya using remote-sensing data and distributed hydrological modeling JF - Advances in water resources N2 - Rivers draining the southern Himalaya provide most of the water supply for the densely populated Indo-Gangetic plains. Despite the importance of water resources in light of climate change, the relative contributions of rainfall, snow and glacier melt to discharge are not well understood, due to the scarcity of ground-based data in this complex terrain. Here, we quantify discharge sources in the Sutlej Valley, western Himalaya, from 2000 to 2012 with a distributed hydrological model that is based on daily, ground-calibrated remote-sensing observation. Based on the consistently good model performance, we analyzed the spatiotemporal distribution of hydrologic components and quantified their contribution to river discharge. Our results indicate that the Sutlej River's annual discharge at the mountain front is sourced to 55% by effective rainfall (rainfall reduced by evapotranspiration), 35% by snow melt and 10% by glacier melt. In the high-elevation orogenic interior glacial runoff contributes ∼30% to annual river discharge. These glacier melt contributions are especially important during years with substantially reduced rainfall and snowmelt runoff, as during 2004, to compensate for low river discharge and ensure sustained water supply and hydropower generation. In 2004, discharge of the Sutlej River totaled only half the maximum annual discharge; with 17.3% being sourced by glacier melt. Our findings underscore the importance of calibrating remote-sensing data with ground-based data to constrain hydrological models with reasonable accuracy. For instance, we found that TRMM (Tropical Rainfall Measuring Mission) product 3B42 V7 systematically overestimates rainfall in arid regions of our study area by a factor of up to 5. By quantifying the spatiotemporal distribution of water resources we provide an important assessment of the potential impact of global warming on river discharge in the western Himalaya. Given the near-global coverage of the utilized remote-sensing datasets this hydrological modeling approach can be readily transferred to other data-sparse regions. KW - Runoff modeling KW - MODIS KW - TRMM KW - Mountain hydrology KW - Sutlej River Y1 - 2016 U6 - https://doi.org/10.1016/j.advwatres.2015.12.004 SN - 0309-1708 SN - 1872-9657 VL - 88 SP - 152 EP - 169 PB - Elsevier CY - Oxford ER -