Weather Radar: Principles and Advanced ApplicationsPeter Meischner Springer Science & Business Media, 06/07/2005 - 337 من الصفحات All aspects of our lives are influenced by the weather and especially severe weather events - such considerations tend to become more pertinent as such weather events may become more frequent with global warming. It is evident that better weather forecasts for the next few hours, days or even weeks have a high economic value for our developed and densely populated world. Ground and air traffic management benefits, as does the construction industry, tourism, agriculture and big outdoor events such as the Olympic games. Weather fore casting nowadays relies on numerical weather prediction models. The reliability and accuracy of forecasts however depends not only on the quality and resolution of these models, but to a high degree on the density and quality of input data from observations. Weather radar measurements complement current data such as ground observations and soundings at smaller scales and satellite observations on larger scales. Targeted weather radar measurements additionally give detailed insight into the processes governing the developments of, for example, frontal systems and thunderstorms, which is needed for improving the model quality by comparing the observations with model forecasts. Weather radar observations on their own can be used directly for short-term forecasting, called nowcasting, just by observ ing the developments of weather systems some 100 km around and extrapolating the observations with knowledge of their typical behaviour. Finally, with reliable quantitative measurements of area precipitation by radar, flood forecasts will be improved. |
المحتوى
The State of Weather Radar Operations Networks and Products | 3 |
12 Doppler Radars | 5 |
13 Polarimetric Radars | 15 |
14 Weather Radar Networking | 19 |
15 Data Quality | 21 |
16 Products of Modern Weather Radars and Networks | 37 |
Operational Measurement of Precipitation in Mountainous Terrain | 54 |
22 Clutter Elimination | 60 |
58 Correction for Attenuation | 160 |
59 Identification of Hydrometeors | 161 |
510 Conclusion | 162 |
References | 165 |
Understanding Severe Weather Systems Using Doppler and Polarisation Radar | 169 |
62 Frontal Systems | 170 |
63 Deep Convective Systems | 174 |
64 The Future | 197 |
23 Correction for Visibility | 65 |
24 Profile Correction | 69 |
25 Adjustments by Gauges | 73 |
26 What Next? | 76 |
References | 77 |
Operational Measurement of Precipitation in Cold Climates | 80 |
32 Problems Due to Ducting over Cold Surfaces | 82 |
33 Precipitation Phase | 92 |
the Main Limiting Factor | 96 |
35 Nowcasting | 106 |
36 Future Outlook | 111 |
37 Acknowledgements | 112 |
Using Radar in Hydrometeorology | 117 |
42 Radar Data Quality Control | 118 |
43 Flood Forecasting | 120 |
44 Coupled Atmospheric and Hydrologic Numerical Models | 125 |
45 Engineering Design | 127 |
46 Future Prospects | 129 |
Improved Precipitation Rates and Data Quality by Using Polarimetric Measurements | 132 |
52 The Polarisation Parameters | 133 |
53 Raindrop Shapes and Size Spectra | 141 |
54 Identification of Ground Clutter and Anomalous Propagation | 146 |
55 Improved Rainfall Rates using Polarisation Parameters | 147 |
56 Improved Rainfall Rate Using Integrated Polarisation Parameters | 152 |
57 Improved Rainfall Rates When Ice May Be Present | 159 |
References | 198 |
Precipitation Measurements from Space | 201 |
72 Statistical Properties of the DropSize Distribution and Parameterisation of the Rain Relations | 206 |
73 Algorithms for Rain Retrieval with a Spaceborne Weather Radar | 217 |
74 Airborne Dual Beam Doppler Radar | 220 |
75 Validation of the Algorithm Product with Airborne or Space Borne Radars | 224 |
Algorithm ZPHI | 228 |
77 The Rain Profiling Algorithm in Future Operational Applications | 234 |
Radar Sensor Synergy for Cloud Studies Case Study of Water Clouds | 237 |
82 Particle Scattering | 238 |
83 Sensor Synergy | 242 |
Retrieval of the Microstructure of Water Clouds | 245 |
85 Acknowledgements | 254 |
Assimilation of Radar Data in Numerical Weather Prediction NWP Models | 257 |
92 Data Assimilation | 258 |
93 Assimilation of Radar Precipitation Data in NWP Models | 261 |
94 Assimilation of Radar Wind Data | 270 |
95 Quality Control of Radar Data for NWP | 275 |
96 Treatment of Radar Data Errors in Assimilation | 276 |
97 Future Prospects | 277 |
References | 278 |
Glossary | 283 |
Color Plates | 315 |
335 | |
طبعات أخرى - عرض جميع المقتطفات
Weather Radar: Principles and Advanced Applications <span dir=ltr>Peter Meischner</span> لا تتوفر معاينة - 2014 |
Weather Radar: Principles and Advanced Applications <span dir=ltr>Peter Meischner</span> لا تتوفر معاينة - 2010 |
عبارات ومصطلحات مألوفة
airborne aircraft algorithm analysis antenna applied areas Atmos attenuation Azimuth backscatter bistatic bright band Bringi C-band calibration Chandrasekar cloud base convective correction data assimilation derived differential phase disdrometer Doppler radar Doppler velocity drop shapes droplet echoes elevation angle error forecast frequency function gauge Germann graupel ground clutter hail height horizontal hydrological hydrometeors Illingworth Joss Koistinen lidar linear liquid water content mesoscale Met Office Meteorological method NORDRAD normalised nowcasting numerical weather prediction Oberpfaffenhofen Oceanic Technol operational parameters particles phase shift polarimetric radar polarisation precipitation Proc propagation pulse radar data radar measurements radar network radar reflectivity radar systems radial velocity rain rate raindrop rainfall rate reflectivity factor region resolution retrieval sampling scan scattering signal spatial squall line storm stratiform supercell target technique Testud thunderstorm transmitted TRMM updraft variables variograms vertical visible wavelength weather radar wind field ZPHI