pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package

북서태평양과 북대서양의 계절 및 가까운 미래 태풍 활동 예측

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 지구환경과학부, 2017. 8. 허창회.Every summertime, tropical cyclone (TC) activity over the worldwide tropical ocean has been receiving large attention due to its destructive impacts on heavily populated countries. To reduce and prepare the potential damages from the TC approach/landfall, development of skillful TC prediction model has been one of the most essential missions for meteorological agency. In this dissertation, the detailed physical relationships between TC activity and environmental fields are investigated. On the basis of these understandings, a track-pattern-based model is developed to predict seasonal to near-future TC activity over the western North Pacific (WNP) and the North Atlantic (NA) basins. This model employs a hybrid statistical–dynamical method and is the first approach to predicts spatial distribution of TC track density covering the entire basin. Thus, it would be a milestone for the prediction of long-term TC track distribution without simulating the climate model. There are three major steps to operate the track-pattern-based model. First, climatological basin-wide TC tracks during the TC season are identified into several patterns using the fuzzy c-means method. Second, the TC counts for each cluster are predicted by using a hybrid statistical–dynamical method. The hybrid prediction for each pattern is based on the statistical relationships (interannual correlation in this thesis) between the seasonal TC frequency of the pattern and the seasonal-mean key predictors dynamically forecast by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Third, the final forecast map of track density is constructed by merging the spatial probabilities of the all clusters and applying necessary bias corrections. The leave-one-out cross validation shows good skill of the WNP TC prediction model, with the correlation coefficients between the hindcasts and the observations ranging from 0.71 to 0.81. The hindcasts of the WNP seasonal TC track density exhibit significant predictability in reproducing the observed pattern. As a real forecast, this model fairly forecast the anomalous spatial distribution of WNP TC track density for the 2010 typhoon season, representing the lowest count since 1951. A higher-than-normal track density was successfully forecast near the East China Sea, Korea, and Japan. The total seasonal TC genesis frequency integrated over the seven patterns is well below normal (about 16.4) close to the observations. The skillful performance in 2010 using the seasonal TC prediction model is attributed to the skillful forecast of the ENSO transition by the NCEP CFS, cooperated with the validity of the prediction model itself. In addition to the WNP basin, a seasonal prediction model of the NA TC activities for the period August–October has been developed on the basis of representative TC track patterns. Using the fuzzy c-means method, a total of 432 TCs are categorized into the following four groups: 1) TCs off the East Coast of the United States, 2) TCs over the Gulf of Mexico, 3) TCs that recurve into the open oceans of the central NA, and 4) TCs that move westward in the southern NA. The model is applied to predict the four TC groups separately in conjunction with global climate forecasts from the NCEP CFSv2. By adding the distributions of the four TC track patterns with pre-calculated TC genesis frequencies, this seasonal TC forecast model provides the spatial distribution of TC activities over the entire NA basin. Multiple forecasts initialized in six consecutive months from February to July are generated at monthly intervals to examine the applicability of this model in operational TC forecasting. Cross-validations of individual forecasts show that the model can reasonably predict the observed TC frequencies over NA at the 99% confidence level. The model shows a stable spatial prediction skill, proving its advantage for forecasting regional TC activities several months in advance. In particular, the model can generate reliable information on regional TC counts in the near-coastal regions as well as in entire NA basin. Among the TC activity, intense TCs accompanying torrential rain and powerful wind gusts often cause substantial socio-economic losses in the regions around their landfall than weak TCs. Thus, we develop the prediction model targeting only intense TCs in the WNP and the NA basins. Different intensity criteria are used to define intense TCs for these two basins, category 3 and above for WNP and category 1 and above for NA, because the number of TCs in the NA basin is much smaller than that in the WNP basin. Using a fuzzy clustering method, intense TC tracks in the WNP and the NA basins are classified into three and two representative patterns, respectively. On the basis of the clustering results, a track-pattern-based model is then developed for forecasting the seasonal activities of intense TCs in the two basins. Generally, the WNP intense TC patterns have predictors of dynamical factor (vertical wind shear or low-level relative vorticity) because of thermally mature state over the WNP to develop the TC whereas the NA intense TCs have thermodynamical factor (sea surface temperature) to the predictor due to the thermally insufficient condition to generate TC over the NA. Cross-validation of the model skill for entire training period as well as verification of a forecast for the 2014 TC season suggest that our intense TC model is applicable to operational uses. Although many studies have attempted to predict TC activities on various time scales, very few focused on near-future predictions. Here we show a decrease in seasonal TC activity over the NA for 2016–2030 using the track-pattern-based TC prediction model. The prediction model is forced by long-term coupled simulations, CFSv2 free runs, initialized using reanalysis data. Unfavorable conditions for TC development including strengthened vertical wind shear, enhanced low-level anticyclonic flow, and cooled sea surface temperature over the tropical NA are found in the simulations. Most of the environmental changes are attributable to cooling of the NA basin-wide sea surface temperature (NASST) and more frequent El Niño episodes in the near future. Consistent NASST warming trend in the Coupled Model Intercomparison Project phase 5 projections suggests that natural variability is still dominant than anthropogenic forcing over the NA in the near-future period.1. INTRODUCTION 1 2. DATA AND METHOD 10 2.1 DATA 10 2.1.1 Tropical cyclone 10 2.1.2 Large-scale environmental fields 11 2.2 METHOD 15 2.2.1 Fuzzy clustering algorithm 15 2.2.2 Track-pattern-based model 17 2.2.3 Upgrade the dynamic part of model from CFSv1 to CFSv2 21 3. SEASONAL PREDICTION OF TROPICAL CYCLONE ACTIVITY 28 3.1 APPLICATION OF THE TRACK-PATTERN-BASED MODEL IN THE WNP 28 3.1.1 Assessment of the 2010 TC season 29 3.1.2 Quasi-real-time operational forecast 49 3.2 PREDICTION OF THE NA SEASONAL TC ACTIVITY 50 3.2.1. Pattern classification of the NA TC tracks 50 3.2.2. Simultaneous relationships and predictors 54 3.2.3. Validation 59 3.2.4. Regional prediction 67 3.3. PREDICTIONS OF INTENSE TC ACTIVITIES IN THE WNP AND NA 73 3.3.1. Definition of intense TC 73 3.3.2 Development of hybrid statistical-dynamical model 79 3.3.3 Real prediction in the 2014 TC season 100 4. NEAR-FUTURE PREDICTION OF TROPICAL CYCLONE ACTIVITY 105 4.1 STRATEGY FOR THE NEAR-FUTURE TC PREDICTION 105 4.1.1 Application of seasonal TC prediction model 105 4.1.2. Multivariate linear regression model using the NASST and Nio 3.4 indices 111 4.2 NEAR-FUTURE PREDICTION OF THE NA TC ACTIVITY 112 4.2.1. Observational responses of the NA TC to NASST and ENSO 112 4.2.2. Prediction results and contributions of the NASST and ENSO 116 4.2.3. Roles of the natural variability and external forcing 130 4.3 NEAR-FUTURE PREDICTION OF THE WNP TC ACTIVITY 134 4.3.1. Prediction results and ENSO contribution 134 4.3.2. Possible influences from other variabilities 137 5. FUTURE STUDY 138 6. CONCLUDING REMARKS 149 REFERENCES 158 국문 초록 179Docto

Applications of Self-Organizing Maps to Statistical Downscaling of Major Regional Climate Variables

This research developed a practical methodological framework, which integrated most of the important aspects related to statistical downscaling. The framework showed high skills when applied to downscale daily precipitation, minimum and maximum temperatures over southeast Australia. Within the framework, self-organizing maps (SOM) algorithm was incorporated as the core technique for interpreting the relationship between the predictor and predictand under consideration following the latest advances in synoptic climatology. The SOM classified large-scale predictors into a small number of synoptic patterns on a physically meaningful basis. By mapping the observed local climate variable (predictand) to these patterns, a downscaling model structure, SOM-SD, was constructed based on the NCAR/NCEP reanalysis data. Moreover, for a new atmospheric state, an ensemble of predictand values was generated by a stochastic re-sampling technique inside the SOM-SD. To improve seasonality of downscaled results, a simple seasonal predictand pool (SPP) scheme was introduced, which can acquire similar skills as the traditional solutions of dividing a year into four seasons. The framework identified and applied a broad suite of statistical indices, including mean, variance, cumulative distribution function (CDF), extreme events to assess the performance of the SOM-SD. In addition, some non-parametric methods were also employed to evaluate the uncertainty of the downscaling approach, which improved its robustness in practice. The quality control of the input data consists of another important component of the framework, which assessed GCM predictors from three aspects: (a) replicate reliably synoptic patterns depicted by the reanalysis data; (b) remain relatively stable in the future; and (c) produce similar downscaling skills as the reanalysis data. Finally, the framework provided an equal-distance CDF mapping method to adjust the discrepancies between the downscaled values and the corresponding observations. This method adjusted the downscaled CDF for the projection period on the difference between the CDFs of the downscaled GCM baseline and observed values. Thus the framework combines the advantages of statistical downscaling model and bias correction method. Moreover, the framework puts a strong emphasis on its flexibility, which underpins its application to other regions, as well as to support impact assessment studies

A review of high impact weather for aviation meteorology

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges

Statistical modelling of wind energy using Principal Component Analysis

The statistical method of Principal Component Analysis (PCA) is developed here from a time-series analysis method used in nonlinear dynamical systems to a forecasting tool and a Measure-Correlate-Predict (MCP) and then applied to wind speed data from a set of Met.Office stations from Scotland. PCA for time-series analysis is a method to separate coherent information from noise of measurements arising from some underlying dynamics and can then be used to describe the underlying dynamics. In the first step, this thesis shows that wind speed measurements from one or more weather stations can be interpreted as measurements originating from some coherent underlying dynamics, amenable to PCA time series analysis. In a second step, the PCA method was used to capture the underlying time-invariant short-term dynamics from an anemometer. These were then used to predict or forecast the wind speeds from some hours ahead to a day ahead. Benchmarking the PCA prediction against persistence, it could be shown that PCA outperforms persistence consistently for forecasting horizons longer than around 8 hours ahead. In the third stage, the PCA method was extended to the MCP problem (PCA-MCP) by which a short set of concurrent data from two sites is used to build a transfer function for the wind speed and direction from one (reference) site to the other (target) site, and then apply that transfer function for a longer period of data from the reference site to predict the expected wind speed and direction at the target site. Different to currently used MCP methods which treat the target site wind speed as the independent variable and the reference site wind speed as the dependent variable, the PCA-MCP does not impose that link but treats the two sites as joint observables from the same underlying coherent dynamics plus some independent variability for each site. PCA then extracts the joint coherent dynamics. A key development step was then to extend the identification of the joint dynamics description into a transfer function in which the expected values at the target site could be inferred from the available measurements at the reference site using the joint dynamics. This extended PCA-MCP was applied to a set of Met.Office data from Scotland and benchmarked a standard linear regression MCP method. For the majority of cases, the error of the resource prediction in terms of wind speed and wind direction distributions at the target site was found to be between 10% and 50% of that made using the standard linear regression. The target mean absolute error was also found to be only the 29% of the linear regression one

Distributed hydrological model using machine learning algorithm for assessing climate change impact

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatio-temporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. An integrated statistical index coupled with a classification optimisation algorithm was used to select coupled model intercomparison project (CMIP6) global climate model (GCMs). Several bias-correction methods were evaluated to identify the best method for downscaling GCM simulations. The study also evaluated the performance of different Satellite-Based Products (SBPs) in replicating observed rainfall to select the best product. A novel two-stage bias correction method were used to correct the bias of the selected SBP. Besides, four widely used bias correction methods were compared to select the best method for downscaling GCM simulations at SBP grid locations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff, and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) located at the south of Peninsular Malaysia was considered as the case study area. The results showed that three GCMs, namely EC-Earth, EC-Earth-Veg and MRI-ESM-2, were the best in replicating the precipitation climatology in mainland Southeast Asia. IMERG was the best among five SBPs with an R2 of 0.56 compared to SM2RAIN-ASCAT (0.15), GSMap (0.18), PERSIANN-CDR (0.14), PERSIANN-CSS (0.10) and CHIRPS (0.13). The two-step bias correction approach improved the performance of IMERG, which reduced the mean bias up to 140 % compared to the other conventional bias correction methods. The method also successfully simulates the historical high rainfall events that caused floods in Peninsular Malaysia. The distributed hydrological model developed using ML showed NSE values of 0.96 and 0.78 and RMSE of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020 - 2059) and the far future (2060 - 2099) for different SSPs. The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as R95TOT, R99TOT, Rx1day, Rx5day and RI, were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The ML based distributed hydrological model developed using the novel two-step bias corrected SBP showed sufficient capability to simulate runoff from satellite rainfall. Application of the ML-based distributed model in JRB indicated that climate change and socio-economic development would cause an increase in the frequency streamflow extremes, causing larger flood events. The modelling framework developed in this study can be used for near-real time monitoring of flood through bias correction near-real time satellite rainfall

Changing horizon of climate science: from scientific knowledge towards demand based, integrated climate services

Els Serveis Climàtics (SC) tenen un rol addicional en la ciència del clima, amb l’objectiu de proporcionals als diferents tipus d’usuaris informació útil y processada sobre variabilitat climàtica, els impactes i riscos del canvi climàtic, així com les oportunitats i incerteses. Per tant, per reduir la distancia entre dades fiables i la seva usabilitat, la investigació de SC és de gran importància. Aquesta tesis d’investigació interdisciplinària aborda el desafiament de sintetitzar la informació climàtica. El seu objectiu general és facilitar la integració d’informació climàtica a escala regional dels SC per donar justificació a la planificació i formulació de polítiques d’adaptació al canvi climàtic. La novetat és què reflecteix l’enfoc orientat als usuaris dels SC, és a dir, a més de l’anàlisi climatològic quantitatiu, també utilitza dades socials qualitatives per entendre millor les necessitats dels professionals i acadèmics involucrats en la co-producció del coneixement relacionat amb el clima. Aquesta tesis utilitza diversos conjunts de dades, incloent dades remotes de la temperatura de la superfície terrestre, dades meteorològiques mesurades en superfície i simulacions de temperatura obtingudes d’un model climàtic regional d’alta resolució (12.5km). Les recomanacions se suporten en l’experiència pràctica. L’estudi de cas a escala local afavoreix nous resultats sobre el risc del calor urbà a la ciutat de València. Revelant un considerable efecte d’illa de calor urbana nocturna juntament amb un confort tèrmic desfavorable a les zones densament urbanitzades. Això subratlla la necessitat d’una planificació urbana resilent amb el canvi climàtic, especialment considerant la tendència d’escalfament gradual projectada per a finals del segle XXI a tota la Península Ibèrica. Per explorar els factors que influeixen en l’eficiència de les col·laboracions transdisciplinaries en els estudis de planificació i adaptació al clima urbà, es realitzaren entrevistes amb acadèmics i professionals. Concloent que la integració de diferents disciplines i perspectives es vital per a l’eficiència dels SC. Una major compressió de les necessitats i motivacions dels actors de les comunitats científiques i professionals contribueix a millorar les prestacions dels SC.Los Servicios Climáticos (SC) desempeñan un rol adicional en la ciencia del clima, con el objetivo de proporcionar a los diferentes tipos de usuarios información útil sobre variabilidad climática, los impactos del cambio climático y sus riesgos, así como las oportunidades e incertidumbres. Por lo tanto, para salvar la brecha entre los datos fiables y su usabilidad, la investigación SC es de gran importancia. El objetivo general de esta tesis de investigación interdisciplinaria es facilitar la integración de información climática a escala regional y local de los SC que apoye la planificación y formulación de políticas de adaptación al cambio climático. La novedad es que refleja el enfoque orientado al usuario del SC, es decir, además del análisis climatológico cuantitativo, también utiliza datos sociales cualitativos para entender mejor las necesidades de los profesionales y académicos involucrados en la co-producción del conocimiento relacionado con el clima. Esta tesis utiliza varios conjuntos de datos, incluyendo datos remotos de la temperatura de la superficie terrestre, datos meteorológicos medidos en superficie y simulaciones de temperatura obtenidas de un modelo climático regional de alta resolución (12,5 km). Las recomendaciones se apoyan en la experiencia práctica. El estudio de caso a escala local ofrece nuevos resultados sobre el riesgo del calor urbano en la ciudad de Valencia. Revelando un considerable efecto de la isla de calor urbana nocturna junto con un confort térmico desfavorable en las zonas densamente urbanizadas. Esto subraya la necesidad de una planificación urbana resiliente al cambio climático, especialmente considerando la tendencia de calentamiento gradual proyecta para finales del siglo XXI en toda la Península Ibérica. Para explorar los factores que influyen la eficiencia de las colaboraciones transdisciplinarias en los estudios de planificación y adaptación al clima urbano, se realizaron entrevistas con académicos y profesionales. Concluyendo que la integración de diferentes disciplinas y perspectivas es vital para la eficiencia de los SC. Una mejor comprensión de las necesidades y motivaciones de los actores de las comunidades científicas y profesionales contribuye a mejorar las prestaciones de los SC.Climate Services (CS) assign an additional role to Climate Science, aiming to provide different kinds of users with usable and actionable information on climate variability, climate change impacts and its related risks, opportunities and uncertainties. Thus, to bridge the gap between reliable data and their usability, CS research is highly important. This interdisciplinary research thesis addresses the climate information distillation challenge. Its overall aim is to pave the way for the integration of regional and local climate information into CS that support climate adaptation planning and policy-making. The novelty of this thesis is that it reflects on the user-oriented approach of CS, i.e., as well as quantitative climatological analysis, it also uses qualitative social data to better understand the needs of practitioners and academics engaged in climate-related knowledge co-production. The thesis uses various datasets, including remotely sensed land surface temperature data, ground-measured meteorological data and temperature simulations obtained from a high resolution (12.5 km) regional climate model. The recommendations are supported by practical experience. The local scale case study offers valuable new insights into the urban heat hazard in the city of Valencia (Spain), revealing the considerable nighttime urban heat island effect along with unfavourable thermal comfort in the densely built-up urban areas. This underlines the need for climate-resilient urban planning, especially in light of the projected gradual warming trend over the entire Iberian Peninsula towards the end of the 21st century. To explore the factors that influence the efficiency of transdisciplinary collaborations working on urban climate adaptation and planning, in-depth interviews were conducted with academics and practitioners. This thesis demonstrated that integrating different disciplines and perspectives is vital for efficient CS. An improved understanding of the needs and motives of stakeholders from science and practice communities greatly contributes to the development of CS