A new algorithm for the retrieval of atmospheric profiles ...wegc · latitude, longitude (bgr), and altitude The ECMWF 24h forecast data are ... ua ba s /N The background uncertainties

大地测量与地球动力学国家重点实验室State Key Laboratory of Geodesy and Earth’s Dynamics

A new algorithm for the retrieval of atmospheric profilesfrom GNSS radio occultation data in moist air conditions

Y. Li1, G. Kirchengast2,3, B. Scherllin-Pirscher2,M. Schwaerz2, J.K. Nielsen4, and Y.B. Yuan1

1 State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics (IGG), Chinese Academy of Sciences, Wuhan, China.2 Wegener Center for Climate and Global Change (WEGC) and Institute for Geophysics, Astrophysics, and Meteorology/Institute of Physics, University of Graz, Graz, Austria.3 Satellite Positioning for Atmosphere, Climate, and Environment (SPACE) Research Centre, RMIT University, Melbourne, Victoria, Australia.4 Danish Meteorological Institute (DMI), Copenhagen, Denmark.

Corresponding author: Ying Li ([email protected])

Thanks for funds to:


Outline

1. Background and motivation

2. Methodology

3. Evaluation and results

4. Summary


Outline


2. Methodology


4. Summary


1.BackgroundGNSS Radio Occultation (RO) technique suffers from a temperature-humidity ambiguity problem in moist air conditions (e.g., < 16 km). In this case, atmospheric profiles cannot be retrieved independently, background profiles have to be used to retrieve moist profiles.

Existing Moist Air Retrieval (MAR) methods:

1. Direct method(s)‒ using additional background data of either temperature or humidity to help

resolve the temperature and humidity ambiguity problem‒ may induce suboptimal uncertainty from backgr. data assumed ‘exactly true’

2. 1D-Var‒ works by finding a maximum likelihood estimate of a vertical atmospheric

state profile x by given a set of observations yo and some prior knowledge ofbackground atmospheric information xb as well as the error covariancematrices of the background and observation information.

‒ computation is sophisticated, depends on correct specs of input uncertainties

xHyOxHyxxBxxx o

1ob

1b 2

121 TTJ

1D-Var method is now used by UCAR and ROM SAF to provide moist profiles


1.MotivationMotivation: atmospheric profilesat low atmos. altitudes in thetroposphere are critical for theapplications of RO data to climateand atmospheric processes research,therefore, we are motivated tointroduce a simple and robustmethod to provide accurateatmospheric profiles in thetroposphere, including adequateuncertainty estimates, andcombining the strength of both“Direct method” and “1D-Var”

Figure produced by Jakob Schwarz


Outline


2. Methodology


4. Summary


The steps of the new (r)OPS moist air algorithm

Main benefits:‒ Avoids sophisticated matrix

inversions but still allows an effective optimal estimation of moist profiles; simple and robust

‒ Allows a straightforward propagation and tracking of uncertainties of all the intermediate and retrieved variables

‒ Seamlessly includes the “direct method” products as by-products

‒ Includes dynamic estimation of the background and observed uncertainties on a daily basis, accounting for the variations with latitude, longitude (bgr), and altitude

The ECMWF 24h forecast data are used to provide background data


Background uncertainties‒ Horizontal resolution 10°× 20° lat/lon grids

‒ Vertical resolution 0.1 km from 0.1 km up to 20.0 km (200 vertical levels)

‒ All variables are generated in each 10°× 20° lat/lon cell on the 200 levels

2/1 2af

2ab zszuzu kkk

Calculation of background uncertainties

Pre-processing and construction of daily updated three dimensional uncertainty fields

2/1 fa,

2a

2aa / Nsbu

The background uncertainties are estimated mainly by calculating the standard deviations of the ECMWF forecast data relative to the ECMWF analysis data. Considering the intrinsic uncertainties that exist in the analysis data, we also empirically account for the systematic uncertainties of analysis data in the estimation of the bgr. uncertainties.

where ua is calculated as:

where ba is calculated from a sys.uncertainty empirical model (Li et al., 2013, 2015).


Bias correction of background profiles

kkk TTT affb

It is useful to mitigate potential short-range forecast biases that may exist in the bgr.Profiles; in the troposphere such biases may exist especially in bgr. humidity profiles.

Similar bias correction for the specific humidity:

afaf qqq

kkk qqq affb

The bias correction steps of temperature are as below:

afaf TTT

(this follows the bias correction approach introduced by Li et al. AMT (2015)as part of the dynamic statistical optimization of bending angles; essentiallyrelaxing the residual bias to the intrinsic analysis biases)


Observation uncertaintiesObservation uncertainty estimation

The observation uncertainty is estimated using an empirical uncertainty model asintroduced by Scherllin-Pirscher et al. AMT (2011). The observation uncertainties below16 km are modeled with some altitude variations. Below the ‘top of the troposphere’, theuncertainty follows an inverse height power law, while above, up to 20 km, the uncertaintywas just modeled as a constant:

where zTtop is selected as 10.0 km, zsbot is selected as 20.0 km, representing average height of the top of the troposphere, and the bottom of stratosphere. s0 is empirically estimated using 0.7 K; q0 is the best fit parameter for the tropospheric model and is empirically estimated as 3 K, p is the exponent and is estimated as 0.5 (cf. Scherllin-P. et al., 2011).

(practically the moist air retrieval algorithm uses this estimate up to 16 km; it is replaced in the rOPS-integrated version by the traceable uncertainties that “come down” from the dry-air retrieval; see the previous talk of Schwarz et al. OPAC-IROWG 2016)

SbotTtop0

TtoppTtop

p00d

for

for 11

zzzs

zzzz

qszT


Characteristics of the estimated dynamic uncertainties:

‒ The observation uncertainties increase quickly below 5 km, in the lower troposphere

‒ The background uncertainties reveal clear latitudinal and seasonal variations

‒ The background temperature uncertainties are larger in polar regions and largest in polar winter

‒ The background specific humidity uncertainties are largest in the tropical and sub-tropical regions

Estimated dynamic uncertainties


Correlations of uncertaintiesCharacteristics of the correlations:

‒ The variations of both observation and background error correlation functions at different heights of the troposphere are relatively small

‒ The correlation lengths of relevant variables are limited to about 1 km to 1.5 km

These results indicate that the correlations are not very significant and can be ignored initially. A further study is on-going to advance this issue, performed within the rOPS.


Outline


2. Methodology


4. Summary


Data and software usedApproaches evaluated － OPSv5.6: the new moist air algorithm, but with static uncertainty profiles, in the form as it is

used in the OPSv5.6 system of WEGC (and contained in the recent 2001–2016 reprocessing).－ Dynamic: the advanced new algorithm that is internally implemented as an advancement of

OPSv5.6 towards rOPS, with hydro-consistent pressure and dynamic uncertainties (obs.uncertainties from OPSv5.6 still static)

－ UCAR: so-called “wet” profiles (from 1D-Var algorithm) downloaded from CDAAC Boulder－ ROM-SAF, profiles with 1D-Var retrieval, also using ECMWF forecasts as background

Evaluation method－ Examine individual RO profiles by comparing with reference profiles－ Examine statistical performance by calculating systematic differences and standard deviations

Data－ simulated MetOp (simMetOp), real observed CHAMP and COSMIC, shown here for test day on

15 July 2008 (14-16 for CHAMP for sufficient events used for statistics).

Software－ (EG)OPSv5.6 software is used for simulating the simMetOp data (EGOPSv5.6), and also for the

retrieval of all WEGC datasets (OPSv5.6, as the retrieval system part of EGOPSv5.6).

Quality control － Standard OPSv5.6 QC, and also eject profiles from CDAAC and ROM-SAF that are denoted by

those QCs as ‘bad profiles’.


Number of profiles available


Individual RO events


Statistical results of simMetOpin Global and TRO region


Statistical results of simMetOpin NHP and SHP region


Statistical results of CHAMP in Global and TRO region


Statistical results of CHAMP in NHP and SHP region


Statistical results of COSMIC in Global and TRO region


Statistical results of COSMIC in NHP and SHP region


Bias correction of background profiles


Outline


2. Methodology


4. Summary


SummaryA new algorithm was developed to provide accurate moist air profiles in the troposphere. It avoids more sophisticated matrix inversions and iterations, which is used in 1D-Var algorithms, but still effectively allows an optimal estimation of moist profiles and a straightforward uncertainty propagation and tracking.

The uncertainties of prescribed background and observed variables are dynamically estimated using statistical calculation and empirical modeling. The estimated uncertainties reveal clear latitudinal and seasonal variations.

The new algorithm was evaluated using simulated MetOp, and real-observed CHAMP and COSMIC data and found to be able to provide a very stable and traceably quality of temperature and humidity profiles from RO in the troposphere, including “direct method” products as by-products, and being a good fit to rOPSrequirements on clear traceabilty of uncertainties.


Thanks for your attention!Questions and comments?

Documents

A new algorithm for the retrieval of atmospheric profiles ...wegc · latitude, longitude (bgr), and altitude The ECMWF 24h forecast data are ... ua ba s /N The background uncertainties