Institut für Physik und Meteorologie

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/3

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Climate dynamics : the performance of seasonal ensemble forecast for improving food security in Ethiopia
(2023) Ware, Markos Budusa; Wulfmeyer, Volker
Part one of this thesis aims to define homogenous climatic regions using objective clustering methods and characterize seasonal cycles, trends, and anomalies in precipitation and temperature. Climate-based on amplifies inherent spatiotemporal climate variability in the Horn of Africa due to global, regional, coastal, and local processes. The homogeneous climatic regions and synoptic circulation types were defined using Principal Component Analysis (PCA) PCA–K-means and PCA–Ward’s. Using the decision criteria of respective algorithms, four homogenous climatic regions were determined for Ethiopia. These climatic regions were distinctive in their seasonal cycles, trends, and anomalies in annual and seasonal precipitation and temperature. These results highlight that the trends in precipitation and temperature vary not only between climatic regions but also by rainy seasons. The short rains (received between November and December) increased by 50 mm/decade in the southwestern region where the evergreen forest meets with the long rainy season. The mean annual and seasonal temperature increased between 0.3 and 0.6 °C/decade virtually in all climatic regions. Regionalization methods were sensitive to spatial domain size but not to the length of the time series. Climatology of sea-level air pressure showed decreasing northward trend over the study domain, as did the temperature, wind velocity, and relative humidity at 500 hPa. However, geopotential height at 500 hPa and temperature at 850 hPa decreased toward the south over the domain. Circulation types were defined by applying PCA on a composite matrix of the six variables. From the first five Principal Components (PCs), ten circulation types (CTs) were defined over East Africa and then associated with environmental events. CTs clearly distinguished rainy seasons comprising different atmospheric states responsible for varying weathers. The summer season was described by a combination of strong positive anomalies in temperature at 850 hPa, northeasterly winds, and Somali jet at 500 hPa, and weak negative anomalies in temperature at 500 hPa. Trends in the number of days categorized in different CTs showed a significant variation among the groups. The drought events, defined using the consecutive dry days (CDD), correspond with positive anomalies in temperature at 850 hPa, northwesterly and Somali Jet, and negative anomalies in relative humidity at 500 hPa. Flooding, defined using a proxy of 80 mm/day per grid cell, was associated with strong westerly winds at 500 hPa, strong positive anomalies in temperature at the lower troposphere, strong easterlies and southwesterly, and positive anomalies in relative humidity at 500 hPa. Part two of the thesis aims to assess the performance of the seasonal ensemble forecast over the Horn of Africa for improving food security. A seasonal forecast with a horizon of up to seven months offers a great opportunity for agricultural optimization, which results in an improved economy and food security. For this purpose, the Weather Research and Forecasting (WRF) model was applied for dynamical downscaling of the latest seasonal forecasting system version 5 (SEAS5) for summer 2018 with different microphysics parameterizations, and initial and boundary conditions. Downscaling was performed by a horizontal resolution of 3 km over the topographically complex domain of East Africa. The seasonal ensemble forecast was evaluated using probabilistic metrics like the Brier skill score, probability ranking score, continuous probability ranking score, discrimination score, and ignorance score. The results of the WRF showed that the model has a strong warm bias in the 2m temperature and a wet bias in precipitation. The relative operating characteristics (ROC) curve showed a higher predicting probability of 2m temperature in below-normal and above-normal terciles over northern Ethiopia and the Indian Ocean, where the model performed better, highlighting the advantage of high-resolution simulations compared to ERA5. The median and distribution of WRF, SEAS5, and ERA5 showed remarkable variation between the homogenous climatic regions. Especially the summer of 2018 was wetter relative to climatology, and WRF overestimated this condition in the region.
Convective-scale data assimilation of thermodynamic lidar data into the weather research and forecasting model
(2022) Thundathil, Rohith Muraleedharan; Wulfmeyer, Volker
This thesis studies the impact of assimilating temperature and humidity profiles from ground-based lidar systems and demonstrates its value for future short-range forecast. Thermodynamic profile obtained from the temperature Raman lidar and the water-vapour differential absorption lidar of the University of Hohenheim during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) project Observation Prototype Experiment (HOPE) are assimilated into the Weather Research and Forecasting model Data Assimilation (WRFDA) system by means of a new forward operator. The impact study assimilating the high-resolution thermodynamic lidar data was conducted using variational and ensemble-based data assimilation methods. The first part of the thesis describes the development of the thermodynamic lidar operator and its implementation through a deterministic DA impact study. The operator facilitates the direct assimilation of water vapour mixing ratio (WVMR), a prognostic variable in the WRF model, without conversion to relative humidity. Undesirable cross sensitivities to temperature are avoided here so that the complete information content of the observation with respect to the water vapour is provided. The assimilation experiments were performed with the three-dimensional variational (3DVAR) DA system with a rapid update cycle (RUC) with hourly frequency over ten hours. The DA experiments with the new operator outperformed the previously used relative humidity operator, and the overall humidity and temperature analyses improved. The simultaneous assimilation of temperature and WVMR resulted in a degradation of the temperature analysis compared to the improvement observed in the sole temperature assimilation experiment. The static background error covariance matrix (B) in the 3DVAR was identified as the reason behind this behaviour. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. The second part of the thesis provides a solution for overcoming the static B matrix issue. A hybrid, ensemble-based approach was applied using the Ensemble Transform Kalman Filter (ETKF) and the 3DVAR to add flow dependency to the B matrix. The hybrid experiment resulted in a 50% lower temperature and water vapour root mean square error (RMSE) than the 3DVAR experiment. Comparisons against independent radiosonde observations showed a reduction of RMSE by 26% for water vapour and 38% for temperature. The planetary boundary layer (PBL) height of the analyses also showed an improvement compared to the available ceilometer. The impact of assimilating a single lidar vertical profile spreads over a 100 km radius, which is promising for future assimilation of water vapour and temperature data from operational lidar networks for short-range weather forecasting. A forecast improvement was observed for 7 hours lead time compared with the ceilometer derived planetary boundary layer height observations and 4 hours with Global Navigation Satellite System (GNSS) derived integrated water vapour observations. With the help of sophisticated DA systems and a robust network of lidar systems, the thesis throws light on the future of short-range operational forecasting.
Sensitivity of land-atmosphere coupling strength in dependence of land cover and atmospheric thermodynamics over Europe
(2023) Jach, Lisa; Wulfmeyer, Volker
Biogeophysical feedbacks between the land surface and the atmosphere have been identified to heavily control the climate system. Land-atmosphere (L-A) coupling strength is a concept to quantify the feedback processes. However, the quantification is still subject to uncertainties, in particular, in the context of land surface influences on local convective precipitation. On the one hand, feedback processes are the result of a chain of complex interactions between various components in the L-A system all exhibiting spatiotemporal variability. On the other hand, L-A coupling strength is not a directly measurable quantity. It can be assessed with different scientific approaches, which makes the quantification dependent on the methodology and the availability of suitable data sets. The aim of this doctoral thesis is to investigate the impact of changes in the vegetation cover and the atmospheric thermodynamic conditions on the long-term coupling signal between the land surface and the triggering of deep moist convection during the European summer. The ‘convective triggering potential – low-level humidity index’ framework, which is a commonly used L-A coupling metric, classifies a day in favor for L-A coupling or not, based on the prevailing thermodynamic conditions in the atmosphere. The daily classifications are used to measure the frequency of days with favorable conditions during the study period, and to identify regions with high frequencies of favorable conditions as coupling hot spots. The framework is applied to model output from regional climate model (RCM) simulations with WRF-NoahMP with diverging land cover conducted over the historical period 1986-2015 for the Euro-CORDEX domain. Impacts of changes in vegetation cover are analyzed by comparing the L-A coupling strength from two sensitivity experiments with idealized extreme land use and land cover changes (LULCCs) against a simulation with realistic land cover. A posteriori modifications to the temperature and moisture output fields of the simulation with realistic land cover were implemented to analyze impacts of systematic changes in the atmospheric thermodynamic conditions. A potential coupling hot spot with predominantly positive feedbacks was identified over Eastern Europe. In Southern Europe and Europe’s coastal areas, the coupling is regularly inhibited by very dry, very wet or stable conditions in the atmosphere. The location of the hot spot appeared insensitive to LULCCs and changes in the thermodynamic conditions. None of the sensitivity tests within a realistic range of temperature and moisture modifications for a recent climate period, led to a disappearance of the hot spot or to overcome the causes for inhibiting coupling in the respective areas in summer. Nevertheless, the experiments demonstrated also considerable variance of the coupling strength within the hot spot region. LULCCs changed the turbulent heat fluxes from the land surface, and thus the atmospheric boundary layer (ABL) heating and moistening. This impacted the boundary layer development of each day. It also caused changes in the average thermodynamic characteristics during the study period, which changed the frequency of favorable pre-conditioning for convection triggering and enhanced the variance in the coupling strength in the hot spot. Both effects were identified to influence the land surface control on the occurrence of convective precipitation. Furthermore, the sensitivity tests with a posteriori modifications revealed uncertainties in the predominant atmospheric response to differently wet surfaces around the Black Sea, shown by a disagreement in the predominant coupling pathway between the modification cases. The findings further indicate uncertainty in whether the hot spot expands over Central Europe, as the feedback signal was sensitive to changes in temperature and moisture. Additionally, the model has a warm and dry bias in this area, which suggests an overestimation of the humidity deficit. The large humidity deficit, in turn, was the inhibiting factor for a high frequency of occurrence of favorable pre-conditions for deep moist convection. The analyses reveal a sensitivity of the L-A coupling strength and atmospheric response to the prevailing land surface and atmospheric conditions in the hot spot. This highlights the need to consider both the land surface state and its impact on L-A coupling strength with respect to predictions of convective precipitation events in strongly coupled regions (and periods). Given that L-A coupling provides predictive skill for climate projections and seasonal forecasts, improved understanding about causes of variability in L-A coupling strength is crucial for improvements therein.

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