1. ISARS, June 2010
Thermodynamic profiling using ground-based microwave radiometry and 1DVAR for nowcasting
DomeNico Cimini
Italian National Research Council (CNR)
Institute for the Environmental Analysis and Monitoring (IMAA)
Randolph Ware(2,3), Jeos Oreamuno(2), Edwin Campos(4), Steve Albers(5,6), Paul Joe(4), Steve Koch(5), Stewart Cober(4)
(2) Radiometrics Corporation, USA
(3) NCAR, CIRES, USA
(4) Environment Canada
(5) NOAA ESRL, USA
(6) CIRA, USA.

2. Outline Microwave (MW) radiometry in a nutshell
Deployment at Vancouver 2010 Winter Olympics
One Dimentional Variational (1DVAR) retrieval
Temperature, humidity and cloud profiling
International network MWRnet
Summary and future work

3. Why microwave radiometry?
Passive technique: natural emission from the atmosphere
Robust, all-weather, unattended instruments
Real time accurate geophysical measurements

4. Atmospheric absorption in the MW
WV-LW channels
O2 (i.e. T) channels
Total
Temperature
(oxygen)
Liquid Water
Water Vapor

5. Basic concepts of MW radiometry
Measurement of thermal emission from:
Gases
in MW mainly oxygen and water vapor
Hydrometeors
in MW ice contribution is negligible
in MW scattering in negligible
Measured brightness temperature (Tb [K]) are processed to estimate atmospheric variables
Ill-posed problem (solution in not unique/stable)
Need for a priori knowledge to constrain the solution
Variety of inversion methods
Regression
Neural Network
Optimal estimation
Variational (1DVAR)

6. What can MW radiometers (MWR) provide?
Highly accurate measurements of integrated contents:
integrated water vapor (IWV)
liquid water path (LWP)
Low resolution vertical profiles:
temperature profiles
higher resolution in the PBL
water vapor density profiles
liquid water content profiles (controversial)

7. What’s the value of MWR profiler for NWP?
Short term forecast skill may be poor due to a lack of timely data, particularly in the lower troposphere where severe weather originates
Timely, accurate atmospheric temperature and humidity data are readily available from MW profilers
Fresh radiometer data can feed well established weather forecast tools and indices developed for radiosondes
Information on cloud liquid helps in identifying opportunity for weather modification and in monitoring and forecasting visibility and icing hazard (such as fog).
State-of-the-art severe weather hazard alerts can be generated using well established forecast tool and index software fed by fresh radiometric temperature, humidity and liquid soundings. The Dubai International Airport is operating a WeatherCam Microwave Profiler as a key element of their Aviation Weather Decision Support System.

8. Status
Continuous thermodynamic profiles including the boundary layer are traditionally retrieved from ground-based MWRP observations using neural network or regression methods.
More recently it has been demonstrated that a One-Dimensional Variational (1DVAR) technique coupling radiometric observations with a numerical weather prediction model output outperforms other temperature and humidity profiling retrieval methods.
This approach avoids error inherent in neural network or regression retrieval methods and benefits from recent surface, radiosonde, satellite, radar and other data residing in the local forecast.

9. 1-Dimention Variational Retrieval (1DVAR)
[After Hewison, 2006]
1DVAR retrievals have been recently demonstrated for ground-based MW radiometers
Lonhert et al. 2004, Hewison 2007, Cimini et al. 2009

10. 1DVAR approach
Nonlinear retrieval technique based on Optimal Estimation Method with a first guess taken from a Numerical Weather Prediction (NWP) model output
NWP Background
(xb,B)
Observations
(y,R)
Retrieval
(xi,A)
Assumptions: Moderately non-linear problem, Gaussian-distributed errors
Method: Gauss-Newton (Newtonian iteration with small residuals)
Cost function*
(to be minimized)
Iterative solution
Error covariance
Convergence
criterium
*Hewison, Standard notation of Ide et al. 1997

11. 1DVAR: Control variables and Jacobians
TEMPERATURE
Control variable:
T (K) at 58 levels (0-10 km)
Jacobian:
K = dF/dT (K/K)
Observations:
50-58 GHz Tb + Tsurf
HUMIDITY
Control variable:
ln(Qt) - Natural logarithm of total
water at 58 levels (0-10 km)
In clear sky, Qt reduces to
Q (specific humidity)
Jacobian:
K = Qt · dF/dQt
Observations:
22-30 GHz Tb + Qsurf
Two steps retrieval: first T, then Q

12. Vancouver 2010 Winter Olympics
During the recent Winter Olympics in Vancouver (Feb 2010) a MWRP was deployed at Whistler, British Columbia, by the Meteorological Service of Canada (MSC).
The continuous thermodynamic soundings at Whistler were used by MSC to support 2010 Vancouver Winter Olympic nowcasting and short term weather forecasting.
From a close-by station, radiosonde were launched at 6–hr interval (4 times/day)
Radiometer retrievals (Neural Network and 1DVAR) were compared with radiosondes to evaluate the retrieval accuracy

13. Vancouver 2010 Winter Olympics
LAPS: NOAA Local Analysis and Prediction System

14. Instrument set up
Instrumentation deployed near the Creekside gondola base
MP-3000A radiometer and other meteorological equipment were located at Timing Flats (776 m altitude) near the base of the Creekside gondola.

17. Results from Vancouver 2010
Temperature profiles
bias/std rms

18. Results from Vancouver 2010
Humidity profiles
bias/std rms

19. Results summary Temperature profiles
1-10 km: rms errors for NN are <5 K, while <1.5 K for 1DVAR. 1DVAR retrievals retain most of the LAPS analysis accuracy.
0-1 km: 1DVAR error is roughly equivalent to NN error The 1DVAR rms error is better than 1.5 K.
Humidity profiles
1-10 km: 1DVAR, NN and LAPS analysis errors are similar.
0-1 km: 1DVAR improves LAPS analysis and approaches zenith NN accuracy in the lower atmosphere. However, off-zenith NN accuracy surpasses 1DVAR in this layer, probably due to NN non-linearity as well as uncertainty in the physical separation of total water into vapor and liquid.
Liquid profiles
LWC profiles are computed from the retrieved total water profile (physical separation of total water into vapor and liquid).

20. Forecast indeces
Nesters radiosonde (red) and radiometer retrievals (blue) soundings (1800 UT 4 Feb 09) and computed forecast indices. Td T
Simultaneous radiometer (blue) and radiosonde (red) temperature (solid) and dewpoint (dashed) soundings at Whistler. The thermodynamic profiler was located 4.4 km SSW of the radiosonde launch site at 117 m higher altitude. In this case fog was detected in the Whistler Valley by the radiosonde, but not by the radiometer on the mountain side.
RDX-RAOB ( skew-T display including forecast indices is shown. RDX-RAOB automatically generates skew-T’s (with forecast indices) and time-height color profiles.

21. WMO_NESTERS, 12-28 Feb 2010 (2 weeks).

22. HgRES, Feb 2010 (2 weeks).

23. 1DVAR, Feb 2010 (2 weeks).

24. MWR international network
Clearly, the 1DVAR approach is ideal for feeding NWP models with radiometer observations
1DVAR could be adopted as the reference approach for a spontaneous international network of MWR that is currently being established.
The ultimate goal would be providing Met Services with near-real-time MWR data and retrievals for data assimilation experiments.
Intermediate steps (defining the “best-practise”)
Calibration/operation protocols
Uniform data formats
Data flow and management

25. An International Network of Microwave Radiometers
About 30 members, more than 50 radiometers worldwide
MWRnet -

26. MWRnet Europe
About 28 radiometers in Europe

27. Summary Thank you very much for your attention!
Statistics show that 1DVAR approach optimally couples the information from model analysis and radiometer observations
The method is totally flexible and suitable for data assimilation in NWP models
Observations are now ready to be assimilated into NWP
1DVAR could be utilized as a reference retrieval for large scale MWR network that is currently being established (MWRnet).
Time is ripe for MWRnet!
Thank you very much for your attention!

28. Summary and future work
MW radiometry is a mature technique
1DVAR approach optimally couples the information from model analysis and radiometer observations
Observations are ready for Data Assimilation in NWP
Time is ripe for MWR international network
Thank you very much for your attention!

29. Back up slides

30. MW radiometer profiler performances
Vertical resolution
(“inter-level error covariance method” (Smith et al., 1999).
T(z)
V(z)
COST720 Temperature, hUmidity and Cloud profiling (TUC) Campaign Payerne (CH)
[Cimini et al., 2006]

31. Integration with other instruments
Instrument integration is promising for enhancing the strengths
and overcome the limitations of each single instrument
MWR + LIDAR
Accurate WV profiles (Han et al.)
MWR + Wind Profiler Radar
WV vertical gradients (Bianco et al.)
Enhanced WV vertical resolution (Klaus et al.)
MWR + IR interferometer
Low IWV and LWP retrievals (Turner et al.)
MWR + Cloud Radar + Ceilometer + …
NWP validation (Illingworth and CLOUDNET team)
Optimal Integration (IPT, Lohnert et al.)

32. Representativeness error

33. NWP user requirements
[After Hewison, 2006]

34. [Güldner and Spänkuch, 2003; Ware et al., 2003]
MWR profiler performances
Radiometer-radiosonde statistics (red and blue) and radiosonde representativeness error (gray)
Radiosonde point measurements have inherent “representativeness error” when they are used to characterize cell volumes in numerical weather models. The representativeness error assigned by NCEP to radiosonde data are shown above in grey. Statistical comparisons of radiometer and radiosonde data are shown in red and blue. Radiometer temperature error is lower in the boundary layer and is slightly higher above 2 km height, whereas radiometer absolute humidity error is lower at all heights. The US Department of Energy takes advantage of this high humidity accuracy by using radiometers to calibrate radiosonde humidity sensors. An photograph of of radiometers used by the DOE to calibrate radiosonde sensors is shown in the following slide.
[Güldner and Spänkuch, 2003; Ware et al., 2003]

35. MW radiometer profiler performances
Multi channels
20-24 GHz - several channels for IWV and WV(z) sensing
24-34 GHz - several channels for LWP and LW(z) sensing
50-60 GHz - several channels for T(z) sensing
Accuracy
~ 0.3 K in Tb
~ 1.0 mm in IWV
~ 0.02 mm in LWP
~ K in T(z)
~ g/m3 in WV(z)
Radiometer-radiosonde statistics
radiosonde representativeness error
[After Güldner and Spänkuch, JAOT, 2003]

36. Basic concepts of MW radiometry
MW radiometry (wavelength ~1 cm):
Good accuracy (eTb ~ 0.3 K)
Can penetrate clouds
Liquid Water Path (LWP) estimates
Rain may be a problem
Mitigation solutions
hydrophobic coating, blowers
All weather

37. Rain Mitigation
Hydrophobic film over the radome