Integrated Ground-Based Remote-Sensing Stations for Atmospheric Profiling: COST Action 720: Final Report

428 Pages·2009·17.43 MB·English

by  Dirk A. M. Engelbart, Wim A. Monna, John Nash

COST – the acronym for European COoperation in Science and Technology –
European Cooperation in
is the oldest and widest European intergovernmental network for cooperation
Science and Technology
in research. Established by the Ministerial Conference in November 1971, COST
is presently used by the scientific communities of 35 European countries to
cooperate in common research projects supported by national funds. ESSEM
The funds provided by COST – less than 1% of the total value of the projects –
support the COST cooperation networks (COST Actions) through which, with
EUR 30 million per year, more than 30 000 European scientists are involved in
Integrated Ground-Based
research having a total value which exceeds EUR 2 billion per year. This is the
financial worth of the European added value which COST achieves.
Remote-Sensing StatioEnSsSE Mfor
A “bottom up approach” (the initiative of launching a COST Action comes from
the European scientists themselves), “à la carte participation” (only countries
Atmospheric Profling
interested in the Action participate), “equality of access” (participation is open
also to the scientific communities of countries not belonging to the European
Union) and “fexible structure” (easy implementation and light management of
the research initiatives) are the main characteristics of COST.

As precursor of advanced multidisciplinary research COST has a very important
role for the realisation of the European Research Area (ERA) anticipating and
complementing the activities of the Framework Programmes, constituting a
$ MPVET
“bridge” towards the scientific communities of emerging countries, increasing
the mobility of researchers across Europe and fostering the establishment of

“Networks of Excellence” in many key scientific domains such as: Biomedicine
and Molecular Biosciences; Food and Agriculture; Forests, their Products and
Services; Materials, Physics and Nanosciences; Chemistry and Molecular
Sciences and Technologies; Earth System Science and Environmental
Management; Information and Communication Technologies; Transport and

Urban Development; Individuals, Society, Culture and Health. It covers basic
and more applied research and also addresses issues of pre-normative nature
or of societal importance.
Web: http://www.cost.esf.org

Cover Pictures:

Time-height cross section of atmospheric water vapour over Lindenberg on
Oct 14/15, 2005 (LAUNCH campaign).

Measurements were started at sunset on Oct 14th. On top, reference data of
the RAMSES lidar are shown while the bottom figure represents NWP forecast
data of the operational meso-scale model ‘LME’ (12 UTC run) of the German
Meteorological Service DWD.
Pictures courtesy of the German Meteorological Service (DWD), Lindenberg Earth System Science and Environmental Management
Observatory.

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Earth System Science and Environmental Management COST Action 720
FINAL REPORT EUR 24172
EC Integrated Ground-Based Remote-Sensing Stations for Atmospheric Profling EUR 24172
QS-NA-24172-EN-C
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Dr Carine PETIT
Science Officer — ESSEM Earth System Science and Environmental Management
INTEGRATED GROUND-BASED
REMOTE SENSING STATIONS
FOR ATMOSPHERIC PROFILING
COST Action 720 – Final Report
Edited by
DIRK A.M. ENGELBART
German Meteorological Service, Richard-Aßmann-Observatory, Lindenberg,
Germany
WIM A. MONNA
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
JOHN NASH
UK MetOffice, Exeter, United Kingdom
CHRISTIAN MÄTZLER
Institute for Applied Physics, University of Bern, Bern, Switzerland
�c European Communities
Luxembourg: Publications Office of the European Union, 2009
CONTENTS
Foreword / Executive Summary
1
Introduction 1
Wim A. Monna
1.1. Background 1
1.2. The COST framework 2
1.3. Objectives and organisation 3
1.4. Results 3
1.5. Outreach activities 6
2
User Needs 7
John Nash et al.
2.1. User Categories 7
2.1.1 Numerical Weather Prediction 8
2.1.2 Nowcasting and Very Short-Range NWP 9
2.2. User Requirements 10
2.2.1 Regional Numerical Weather Prediction 10
2.2.2 Nowcasting 12
2.2.3 Global Climate Monitoring 13
2.3. Discussion 13
2.3.1 Random vs. Systematic Errors 14
2.3.2 Implications for network density 15
2.3.3 Direct vs. indirect observations? 15
3
Basic Techniques 17
3.1. Rationale for Sensor Synergy 17
Herman W. J. Russchenberg and Dirk A. M. Engelbart
3.2. Microwave Radiometers 20
Christian Mätzler et al.
3.2.1 Introduction 20
3.2.2 Physical Principles of Microwave Radiometry 21
3.2.3 Microwave Absorption and Emission 23
3.2.4 Frequency Allocations 29
3.2.5 Retrieval Techniques 29
3.2.6 Radiometer Techniques 32
iii
3.2.7 Examples of Radiometer Systems 38
3.2.8 Complementary Sensors 44
3.2.9 Financial Constraints 45
3.2.10 Long-Term Monitoring 46
3.3. Lidar 61
Ulla Wandinger
3.3.1 Status of selected lidar techniques 63
3.3.2 New and improved methods 83
3.4. Fourier Transform Infrared Radiometer 95
Jens Reichardt and Jürgen Güldner
3.4.1 Fourier Transform Infrared Spectroscopy 95
3.4.2 EISAR 96
3.4.3 Retrieval technique and measurement examples 98
3.4.4 Outlook 101
3.5. Windprofiler / RASS 104
Hans Richner et al.
3.5.1 Basic algorithms for wind profiling 104
3.5.2 Improved temperature profiling 127
3.5.3 Algorithms for humidity profiling 131
3.6. Sodar / RASS 138
Erich Mursch-Radlgruber et al.
3.6.1 Introduction 138
3.6.2 RASS an extension to sodar 141
3.6.3 Typical applications and limitations 142
3.6.4 Acoustic cross section - back scatter intensity 142
3.6.5 Mean wind and variances 143
3.6.6 Mixing height 143
3.6.7 Turbulence variables and fluxes 144
3.7. Cloud radar 149
Ulrich Görsdorf
3.7.1 Status of cloud radars 148
3.7.2 Parameters which can be derived from cloud radar
measurements 160
3.8. Weather radar & Ceilometer 167
Jani Poutiainen
3.8.1 Introduction 167
3.8.2 Weather radar 167
3.8.3 Comparisons of weather radar and ceilometer 170
3.8.4 Measurement arrangements 172
3.8.5 Results and conclusions 173
3.8.6 Summary 177
iv COST 720
3.9. Meteorological Applications of GPS satellites 179
M. Mauprivez, V. Klaus, and P. Hereil
3.9.1 Introduction 179
3.9.2 GPS water-vapour measurement 179
3.9.3 Radio-Occultation Technique 184
3.9.4 Results of the COST-716 Action 185
3.9.5 Conclusion 187
Appendix 189
4
Integrated Profiling 195
4.1. Radar-Lidar synergy 195
Oleg A. Krasnov and Herman W. J. Russchenberg
4.1.1 Introduction 195
4.1.2 Principle of the radar-lidar technique 196
4.1.3 The application to CloudNet data 198
4.1.4 Conclusions 201
4.2. Synergy of Cloud radar and Ceilometer 203
Daniela Nowak
4.2.1 Introduction 203
4.2.2 Instrumentation and method 203
4.2.3 Results 204
4.2.4 Discussion and Conclusion 208
4.3. Integration of UHF WPR and MW radiometer 211
Laura Bianco et al.
4.3.1 Abstract 211
4.3.2 Introduction 212
4.3.3 Basic principles 212
4.3.4 Instruments and data processing 213
4.3.5 Case study 215
4.3.6 Summary and further developments 216
4.4. Integration of MW and Thermal-IR Radiometers 220
Christian Mätzler et al.
4.4.1 Infrared radiometers 220
4.4.2 Infrared radiometers for cloud monitoring 221
4.4.3 Synergies between MW and IR radiometer data 221
4.4.4 Technical requirements 224
4.5. Combining WPR and MWR 226
Vladislav Klaus
4.5.1 Introduction 226
4.5.2 The Tsuda Technique 226
v
4.5.3 Practical implementation 227
4.5.4 Experimental site and instrumentation 229
4.5.5 Results 231
4.5.6 Discussion and conclusions 233
4.6. WPR SNR and TUC profiles 237
Catherine Gaffard and John Nash
4.6.1 Introduction 237
4.6.2 Scattering mechanisms 238
4.6.3 Relating vertical structure in refractive index to tur-
bulent structure parameters 240
4.6.4 Experimental data 243
4.6.5 Moments analysis of the TUC data, 2003/2004 253
4.6.6 Further work and recommendations 265
4.7. 1D-VAR 268
Tim J. Hewison
4.7.1 Introduction 268
4.7.2 Background Data and State Vector 269
4.7.3 Observations 270
4.7.4 Forward Model and its Jacobian 271
4.7.5 Error Analysis 272
4.7.6 Minimization of Cost Function 274
4.7.7 Example 1D-VAR Retrievals 276
4.7.8 Cloud Classification Scheme 276
4.7.9 Statistics of 1D-VAR Retrievals 277
4.7.10 Conclusions and Future Work 278
5
Field Experiments 281
5.1. The TUC Campaign 281
Dominique C. Ruffieux
5.1.1 Introduction 281
5.1.2 In-situ and remote sensing systems involved 282
5.1.3 Summary 286
5.2. LAUNCH-2005 290
Dirk A. M. Engelbart and Edwin Haas
5.2.1 Introduction 290
5.2.2 Assessment of basic techniques and algorithms 291
5.2.3 Assessment of algorithms for integrated profiling 295
vi COST 720
5.2.4 OSEs using networks of water-vapour lidars 296
5.2.5 Model validation using high-precision remote sens-
ing 297
5.2.6 Evaluation of a new moisture advection and time in-
tegration scheme in MM5 298
5.3. CSIP campaign 308
Judith Agnew
5.4. Helsinki Testbed 315
Jani Poutiainen et al.
5.4.1 Description of Helsinki Testbed 315
5.4.2 Measurement campaigns and climate summary 317
5.4.3 Integration of technologies 318
5.4.4 Data analysis 320
5.4.5 Conclusions 329
5.5. WMO Intercomparison of High-Quality Radiosondes 330
John Nash
5.5.1 Introduction 330
5.5.2 Results from equipment supported by COST 720 331
5.5.3 Radiosonde test results relevant to COST 720 337
5.5.4 Lessons Learned 340
6
Data Assimilation 341
Hans-Stefan Bauer et al.
6.1. Introduction 341
6.2. LAUNCH: 3DVAR OSE 344
Rossella Ferretti and Claudia Faccani
6.2.1 Variational Assimilation Technique: 3DVAR 345
6.2.2 Meteorological event 345
6.2.3 Model set up and experiments 346
6.2.4 Results 349
6.2.5 Conclusions 351
6.3. LAUNCH: 4DVAR OSE 354
Hans-Stefan Bauer and Matthias Grzeschik
6.3.1 The 4DVAR method for LAUNCH-2005 354
6.3.2 Introduction to MM5 and its 4DVAR system 356
6.3.3 Assimilation Experiments during LAUNCH-2005 358
6.3.4 Outlook 368
vii
7
Conclusions 371
John Nash and Catherine Gaffard
7.1. Management and Action Structure Issues 371
7.2. Progress with Basic Techniques and Algorithms 372
7.2.1 Microwave radiometers 372
7.2.2 Lidars [water vapour, wind, cloud and aerosol] 373
7.2.3 Wind profiler radars 373
7.2.4 Cloud radar [pulsed and fm-cw] 373
7.2.5 C-band weather radar 373
7.3. Progress with integration of individual systems into one pro-
filing station 374
7.3.1 Derivation of additional parameters 374
7.3.2 Data retrieval/ assimilation techniques 375
7.3.3 Initiation of a EUMETNET observatory-type network
project 375
7.3.4 Cost effective profiling for operational networks 376
List of Tables 377
List of Figures 380