Precipitation Science measurement, remote sensing, microphysics and modeling$dedited by Silas Michaelides (Climate and Atmosphere Research Center (Care-C), The Cyprus Institute, Nicosia, Cyprus)

__Precipitation Science:__ Measurement, Remote Sensing, Microphysics and Modeling addresses the latest key concerns for researchers in precipitation science, mainly observing, measuring, modeling and forecasting. Using case studies and global examples, the book demonstrates how researchers are addressing these issues using state-of-the-art methods and models to improve accuracy and output across the field. In the process, it covers such topics as discrepancies between models and observations, precipitation estimations, error assessment, droplet size distributions, and using data in forecasting and simulations. Other sections cover improved standard approaches, novel approaches, and coverage of a variety of topics such as climatology, data records, and more. By providing comprehensive coverage of the most up-to-date approaches to understanding, modeling, and predicting precipitation, this book offers researchers in atmospheric science, hydrology and meteorology with a comprehensive resource for improving outcomes and advancing knowledge. Cover Precipitation Science Copyright Dedication Contents Foreword Preface Related titles by Silas Michaelides References Credits Part 1 Precipitation Measurement 1 Accuracy assessment and intercomparison of precipitation measurement instruments 1.1 Introduction 1.2 Precipitation measurement biases 1.2.1 Instrumental biases and calibration procedures for catching gauges 1.2.1.1 Field calibration device 1.2.2 Calibration of non-catching gauges 1.3 Wind-induced bias of catching gauges 1.3.1 Computational fluid dynamic simulations 1.3.2 Particle tracking model and collection efficiency curves 1.4 Intercomparison of precipitation measurement instruments 1.5 Concluding remarks References 2 Application of underwater passive acoustic measurements of ocean sound in precipitation estimation 2.1 Introduction 2.2 Passive aquatic listening technology, methods, and data collection 2.3 Acoustic data analysis 2.3.1 Data collection 2.3.2 Acoustic data and quality control processing 2.3.2.1 Electronic filter correction 2.3.2.2 Residual sensitivity correction 2.3.2.3 Time series of acoustic parameters 2.3.2.4 Classification analysis 2.3.2.5 Acoustic wind speed measurement 2.4 Acoustic precipitation analysis 2.5 Case studies 2.5.1 The Ionian Sea rainfall experiment 2.5.1.1 Classification and validation procedure 2.5.1.2 Assessment results: case study on March 12, 2004 2.5.1.3 Spatial averaging of the rainfall signal 2.5.2 The Aegean Sea experiment 2.5.2.1 Marine mammal detection 2.5.2.2 Shipping detection 2.5.2.3 Sound budgets and acoustic summaries 2.6 Concluding remarks References 3 Quality control and verification of precipitation observations, estimates, and forecasts 3.1 Introduction 3.2 Quality control of observations from a rain gauge network 3.2.1 Rain gauge errors 3.2.2 Rain gauge data quality control 3.2.3 Examples of implementation of procedures for quality control of rain gauge data 3.2.4 Increasing rain gauge network density by applying other techniques 3.3 Quality control of weather radar data 3.3.1 Quality characterization of radar data 3.3.2 Quality control of 3D radar data 3.3.3 Quality control of 2D surface precipitation estimates 3.3.4 Quality-based composition of 2D surface precipitation products 3.4 Quality control of satellite observations 3.4.1 Observations of precipitation from meteorological satellites 3.4.2 Quality control of precipitation estimates based on satellite products 3.5 Quality control of multisource surface precipitation estimates 3.5.1 Multisource precipitation estimates 3.5.2 Quality-based multisource precipitation estimation 3.5.3 Example of merging 3.6 Methods of evaluating the skill of forecasts 3.6.1 Precipitation forecasts 3.6.1.1 Introduction 3.6.1.2 Errors in NWP modeling of precipitation 3.6.1.3 Observational data 3.6.1.4 Verifying models using observational data: synoptic stations, radars, and satellites 3.6.1.5 Verification measures and methods 3.6.2 Standard methods of forecast verification 3.6.3 Spatial methods of forecast verification 3.7 Conclusion References 4 Insights on a global Extreme Rainfall Detection System 4.1 Introduction 4.2 The Extreme Rainfall Detection System: input data 4.2.1 Near real-time 4.2.2 Forecast 4.3 Extreme rainfall detection methodology 4.4 Case studies 4.5 Conclusion Acknowledgments References Part 2 Precipitation Remote Sensing 5 Evaluation of high-resolution satellite precipitation data over the Mediterranean Region 5.1 Introduction 5.2 Study area 5.3 Data and methodology 5.3.1 TRMM/GPM data 5.3.2 GSMaP data 5.3.3 E-OBS data 5.3.4 Methods 5.4 Results and discussion 5.4.1 Mean annual precipitation maps 5.4.2 Average difference maps 5.4.3 Correlation maps 5.5 Conclusion Acknowledgments References 6 Fundamental satellite precipitation data records 6.1 Introduction 6.2 Satellite precipitation estimates 6.3 Satellite observational records 6.4 Precipitation climate data records 6.5 Key questions 6.6 Conclusion Acknowledgments References 7 The potential of using satellite-related precipitation data sources in arid regions 7.1 Arid regions 7.2 Challenges of arid regions 7.2.1 Water scarcity 7.2.2 Data scarcity 7.3 The water cycle in arid regions 7.3.1 Precipitation 7.3.2 Infiltration 7.3.3 Runoff 7.3.4 Evapotranspiration 7.4 Storage 7.4.1 Aquifers 7.4.2 Soil moisture 7.4.3 Rivers and lakes 7.5 Water consumption 7.6 Satellite-based precipitation data sources 7.7 Performance of satellite-related precipitation estimations in an arid region 7.7.1 The study site 7.7.2 Rain-gauge network and in situ measurements 7.7.3 TMPA and IMERG precipitation data 7.7.4 Statistical metrics 7.7.4.1 Statistical tests with TMPA and IMERG 7.7.4.2 Compatibility of TMPA and IMERG data to rain-gauge measurements 7.7.4.3 TMPA and IMERG data in detecting rainfall 7.7.5 Discussion of results 7.8 Concluding remarks Acknowledgments References 8 Monitoring precipitation from space: progress, challenges, and opportunities 8.1 Introduction 8.2 Progress in satellite-based precipitation monitoring 8.3 Gaps, challenges, and opportunities 8.3.1 Challenges 8.3.2 Downscaling 8.3.3 Error correction 8.3.4 Satellite-based precipitation applications 8.3.5 Water resource management 8.3.6 Drought prediction 8.3.7 River flow forecast 8.3.8 Landslide forecast 8.3.9 Numerical weather forecast 8.4 Conclusion References 9 Satellite hail detection 9.1 Introduction 9.2 Physical basis underpinning hail remote sensing 9.2.1 Radar remote sensing 9.2.2 Radiometer remote sensing 9.3 State-of-the-art satellite microwave methods for hail detection 9.3.1 Satellite radar-based detection of hail 9.3.2 Satellite radiometer-based detection of hail 9.4 Satellite observations: July 17, 2019 case study 9.5 Satellite climatology of hail: status, pitfalls, and ways forward 9.5.1 Champion storms 9.5.2 Hail climatologies 9.6 Conclusion and future perspectives Acknowledgments References 10 Development of a precipitation-retrieval scheme for cross-track passive microwave sounding instruments 10.1 Introduction 10.2 Precipitation retrievals 10.3 Development of the Precipitation Retrieval and Profiling Scheme 10.3.1 The PRPS-SAPHIR a priori scheme 10.3.2 Algorithm design 10.3.3 The PRPS DPR-SAPHIR database 10.3.4 PRPS retrieval 10.4 Evaluation and validation 10.5 Future directions References 11 Evaluation of high-resolution satellite precipitation over the global oceans 11.1 Introduction 11.2 Datasets 11.2.1 OceanRAIN dataset 11.2.2 IMERG dataset 11.2.3 Matched dataset 11.3 Evaluation procedure 11.4 Discussion of evaluation results 11.4.1 IMERG–OceanRAIN comparison 11.4.2 Evaluation of error sources 11.5 OceanRAIN applications 11.6 Conclusion References 12 Recent advances and challenges in satellite-based snowfall detection and estimation 12.1 Introduction 12.2 Spaceborne radars and snowfall 12.2.1 CloudSat Cloud Profiling Radar 12.2.2 The GPM-CO dual-frequency precipitation radar 12.3 Passive microwave radiometry and snowfall 12.3.1 GMI and ATMS snowfall observation capabilities 12.3.1.1 GMI high-frequency channels and 166-GHz polarization signal 12.3.1.2 Impact of background surface conditions 12.3.1.3 Analysis of ATMS snowfall observation capabilities 12.4 PMW snowfall retrieval techniques 12.4.1 GPROF and SLALOM snowfall retrieval algorithms for GMI 12.5 Ground-based snowfall observations 12.6 Conclusion and recommendations Acknowledgments References 13 Errors and uncertainties associated with quasiglobal satellite precipitation products 13.1 Introduction 13.2 Sensor errors and uncertainties 13.3 Retrieval scheme errors and uncertainties 13.3.1 Information from observations 13.3.2 Incorporating ancillary data 13.4 Product errors and uncertainties 13.5 Conclusion References 14 Performance assessment of merged multisatellite precipitation datasets over diverse climate and complex topography 14.1 Introduction 14.2 Data and methodology 14.2.1 Study area 14.2.1.1 Glacial zone 14.2.1.2 Humid zone 14.2.1.3 Arid zone 14.2.1.4 Hyper-arid zone 14.2.2 Spatial distribution of precipitation across all climate zones of Pakistan 14.3 Performance assessment of satellite precipitation products across Pakistan 14.3.1 Introduction to available assessment results 14.3.2 Limitations, controversies, and intercomparison of zonal errors in evaluated satellite precipitation products 14.4 Merged precipitation datasets: advancements and imperfections 14.4.1 Glacial zone 14.4.2 Humid zone 14.4.3 Arid zone 14.4.4 Hyperarid zone 14.5 Conclusion Acknowledgments References Part 3 Precipitation Microphysics 15 Melting of atmospheric ice particles 15.1 Introduction 15.2 Theoretical concept and quantitative description of atmospheric melting 15.3 Theory of hail melting in the 1950s 15.4 Requirements of laboratory experiments 15.5 Laboratory experiments on hailstones, graupel, ice crystals, and snowflakes 15.5.1 Experiments in the 1960s: hailstones and ice spheres 15.5.2 Experiments in the 1980s with L-type and portable wind tunnels: snowflakes 15.5.3 Experiments in the 1980s with a Z-type wind tunnel: hailstones and graupel 15.5.4 Experiments in the early 1990s with a Z-type wind tunnel: snowflakes 15.5.5 Experiments in the 2000s with a horizontal diffusion chamber: ice and snow crystal 15.5.6 Experiments in the 2010s with acoustic levitators: irregular and spheroidal ice particles 15.5.7 Experiments 2018–20 with a Z-type wind tunnel: hailstones 15.6 Summary and atmospheric implications Acknowledgments References 16 Variability of raindrop size distribution as characterized by the generalized gamma formulation 16.1 Introduction 16.2 Generalized gamma distribution for DSDs 16.2.1 GG model and fitting to measured DSDs 16.2.2 Brief overview of prior results 16.3 Data from the Greeley and Huntsville campaign 16.4 The stability of h(x) 16.5 Applications of the modal GG-fitted h(x) 16.6 Summary and conclusion Acknowledgments Appendix Statistical inference Model selection SGG performance References 17 Rainfall microphysical characterization over the Mediterranean area during the GPM era 17.1 Introduction 17.2 Data and instrumentation 17.2.1 DPR overview and products 17.2.2 Ground-based radar network 17.2.3 Two-dimensional video disdrometer 17.3 Preliminary analysis: single- versus dual-frequency DPR comparison 17.3.1 Data selection 17.3.2 DF- and SF-based DPR standard outputs comparison 17.4 DF- and SF-based rain and DSD parameters comparison at NSL 17.5 Validation of microphysical and integral DSD parameters 17.5.1 Microphysical and integral DSD parameters estimation from GR data 17.5.2 The neural network approach 17.5.3 The empirical relationships approach 17.5.4 A preliminary analysis on the derivation of the DSD parameters 17.5.5 DPR and DPR–GMI performance 17.5.6 Sensitivity analysis 17.6 Conclusion References 18 Cloud processes of the main precipitating systems over continental tropical regions 18.1 Introduction 18.2 Brazil rainfall regimes and the CHUVA experiment overview 18.3 The ground-based reference precipitation datasets 18.4 Assessments, validation, and characterization of GPM rainfall retrievals 18.5 Cloud microphysical properties: results of an hydrometeor classification over the Amazonian region 18.6 Cloud properties and associated electrification features over Brazil 18.6.1 General characteristics on clouds electrification and lightning 18.6.2 Spatial–temporal distribution of lightning in the tropics 18.6.3 Cloud microphysical properties and lightning 18.6.4 Severe weather nowcasting 18.7 Contribution of the aerosol in the process of formation of precipitation 18.7.1 Aerosol effects on the formation of Amazonian clouds 18.7.2 Shallow cumulus phase 18.7.3 Deep convection References 19 “Moisture control” of the precipitation: a probabilistic perspective 19.1 Introduction 19.2 Data description 19.3 Analysis methodology and probability theory 19.3.1 Column precipitable water 19.3.2 Probability theory 19.3.3 Normalization 19.4 Results 19.5 Further discussions Acknowledgments References Part 4 Precipitation Modeling 20 Precipitation in Earth system models: advances and limitations 20.1 Relevance of precipitation in climate models 20.2 Data sources and basic comparisons 20.2.1 Annual mean 20.3 Diurnal to seasonal variabilities 20.3.1 Diurnal cycle 20.3.2 Intraseasonal and seasonal cycles 20.3.2.1 Madden–Julian oscillation 20.3.2.2 Monsoon 20.4 Interannual variability 20.4.1 Time series 20.4.2 El Niño–Southern oscillation 20.4.3 Indian Ocean Dipole 20.5 Extreme events 20.5.1 Extreme precipitation 20.5.2 Tropical cyclones 20.6 Role of spatial resolution in climate models 20.6.1 Parameterizations 20.6.2 Dynamic downscaling 20.7 Climate model tuning 20.8 Concluding remarks Acknowledgments References 21 Improvement of quantitative precipitation forecast at the short range through lightning data assimilation 21.1 Introduction 21.2 Method 21.2.1 The RAMS@ISAC and its configuration for the very short-term forecast 21.2.2 Lightning assimilation through nudging 21.2.3 Lightning assimilation through 3D-Var 21.2.4 Lightning and raingauge data 21.3 The application to two case studies 21.4 An application for a whole year 21.5 Sensitivity of the assimilation scheme to lower boundary formulation 21.6 The impact of forecasting time on the performance 21.7 Conclusion Acknowledgments Appendix A References 22 Multiplatform hydrometeorological analysis of a flash flood event 22.1 Introduction 22.2 Flash flood events in Greece 22.2.1 The flash flood event of Mandra, Attica, Greece 22.3 The integrated hydrometeorological system and remote sensing data 22.3.1 CHAOS modeling system 22.3.2 Meteorological model setup 22.3.3 Hydrological model setup 22.3.4 LAPS data assimilation system and precipitation advection 22.3.5 X-band dual-polarization high-resolution precipitation data 22.3.5.1 Dual-polarization weather radar technology 22.3.5.2 X-band dual-polarization systems 22.3.6 The National Observatory of Athens X-band dual-polarization radar 22.3.7 GPM/IMERG satellite data 22.4 Methodological approach 22.4.1 Design of numerical experiments and nowcasting procedure 22.4.2 Evaluation methodology 22.5 Results and evaluation 22.5.1 Quantitative comparison of precipitation, discharge, and water level results 22.5.2 Brief discussion and comparison with other studies 22.5.3 Qualitative evaluation of estimated flood extent 22.5.4 Nowcasting of precipitation and discharge 22.5.5 Evaluation of the nowcasting approach 22.6 Concluding remarks Acknowledgments References 23 Radar data assimilation in numerical weather prediction models 23.1 Introduction 23.2 Radar data assimilation 23.2.1 Variational method 23.2.2 Precipitation assimilation 23.2.3 Indirect assimilation 23.2.4 Direct assimilation 23.3 Conclusion References 24 Direct assimilation of radar reflectivity volumes in COSMO–LETKF 24.1 Introduction 24.2 Numerical weather prediction model and data assimilation system 24.2.1 COSMO model 24.2.2 Data assimilation via KENDA–LETKF 24.2.2.1 Ensemble Kalman filter 24.2.2.2 The kilometer-scale ensemble data assimilation system 24.2.3 Efficient modular volume radar operator 24.2.4 Latent heat nudging 24.3 Open issues on the direct assimilation of radar reflectivity volumes 24.3.1 Thinning and averaging 24.3.2 Dealing with no precipitation observations 24.3.3 Length of assimilation cycles 24.3.4 Observation errors 24.4 Impact of assimilating reflectivity volumes References 25 Enhancing data assimilation of GPM observations 25.1 Introduction 25.2 The NICAM-LETKF system 25.3 NICAM-LETKF JAXA Research Analysis 25.3.1 Overview 25.3.2 July 2018 heavy rain event in Japan 25.3.3 Merged forecast with GSMaP nowcasting 25.4 Ensemble Forecast Sensitivity to Observations 25.5 Model parameter estimation by data assimilation 25.6 Summary References List of abbreviations Index Backcover Precipitation Science: Measurement, Remote Sensing, Microphysics and Modeling addresses the latest key concerns for researchers in precipitation science, mainly observing, measuring, modeling and forecasting. Using case studies and global examples, the book demonstrates how researchers are addressing these issues using state-of-the-art methods and models to improve accuracy and output across the field. In the process, it covers such topics as discrepancies between models and observations, precipitation estimations, error assessment, droplet size distributions, and using data in forecasting and simulations. Other sections cover improved standard approaches, novel approaches, and coverage of a variety of topics such as climatology, data records, and more. By providing comprehensive coverage of the most up-to-date approaches to understanding, modeling, and predicting precipitation, this book offers researchers in atmospheric science, hydrology and meteorology with a comprehensive resource for improving outcomes and advancing knowledge. Provides updated and novel approaches to key issues in precipitation research Offers practical knowledge through global examples and case studies Includes full-color visuals to enhance comprehension of key concepts