CHAllenging Minisatellite Payload Data Research Articles

CHAMP climate data based on the inversion of monthly average bending angles

Abstract. Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity climatologies for the stratosphere can be obtained from the Abel inversion of monthly average bending-angle profiles. The averaging of large numbers of profiles suppresses random noise and this, in combination with simple exponential extrapolation above an altitude of 80 km, circumvents the need for a "statistical optimization" step in the processing. Using data from the US–Taiwanese COSMIC mission, which provides ~1500–2000 occultations per day, it has been shown that this average-profile inversion (API) technique provides a robust method for generating stratospheric refractivity climatologies. Prior to the launch of COSMIC in mid-2006, the data records rely on data from the CHAMP (CHAllenging Mini-satellite Payload) mission. In order to exploit the full range of available RO data, the usage of CHAMP data is also required. CHAMP only provided ~200 profiles per day, and the measurements were noisier than COSMIC. As a consequence, the main research question in this study was to see if the average bending-angle approach is also applicable to CHAMP data. Different methods for the suppression of random noise – statistical and through data quality prescreening – were tested. The API retrievals were compared with the more conventional approach of averaging individual refractivity profiles, produced with the implementation of statistical optimization used in the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Radio Occultation Meteorology Satellite Application Facility (ROM SAF) operational processing. In this study it is demonstrated that the API retrieval technique works well for CHAMP data, enabling the generation of long-term stratospheric RO climate data records from August 2001 and onward. The resulting CHAMP refractivity climatologies are found to be practically identical to the standard retrieval at the DMI (Danish Meteorological Institute) below altitudes of 35 km. Between 35 and 50 km, the differences between the two retrieval methods started to increase, showing largest differences at high latitudes and high altitudes. Furthermore, in the winter hemisphere high-latitude region, the biases relative to ECMWF (European Centre for Medium-range Weather Forecasts) were generally smaller for the new approach than for the standard retrieval.

Band-Limited Analysis of Current Satellite-to-Satellite Tracking, Gradiometry and Combined Earth Gravity Models

Several satellite-only gravity models based on the analysis of satellite-to-satellite tracking (SST) data have become available in the course of the last decade. The realization of the satellite missions CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery And Climate Experiment (GRACE) enabled the practical implementation of two modes of the SST principle, namely the high–low and the low–low SST. Though similar in their fundamental idea, which is the indirect observation of the gravity field based on the position of two satellites orbiting the Earth, the different architecture and geometrical layout of these techniques capture different fingerprints of the observed field. In the last few years, satellite-only gravity models based on the analysis of satellite gravity gradiometry (SGG) data became available and led to a new insight into the gravity field. The implementation of the SGG principle became possible after the launch of Gravity field and steady-state Ocean Circulation Explorer (GOCE), the first gravitational gradiometry mission. Based on the principle of differential accelerometry, GOCE provides the gravitational gradients which can be used in gravity field retrieval as primary observations of the field at satellite altitude. In the present study, we consider some of the current satellite-only and combined gravity models based on the analysis of CHAMP, GRACE, GOCE, gravimetry and altimetry data. In order to perform a thorough analysis of the models, we present an overview of tools for their quality assessment both in an absolute and relative sense in terms of computing spectral quantities, such as correlation or smoothing coefficients per degree and per order, attempting to demonstrate possible non-isotropic features in the models. Furthermore, typical geodetic measures in computing second-order derivatives, such as degree and order variances and difference variances, have been also evaluated for the same models, using the combined model EGM2008 as reference. Apart from these standard spectral assessment quantities, a systematic spatial representation of the second derivatives at satellite altitude has been performed. The combination of the two analysis steps (spectral and spatial) permits a first detailed assessment of the models, focusing especially on the identification of characteristic interpretable bandwidths.

Reproducibility of GPS radio occultation data for climate monitoring: Profile‐to‐profile inter‐comparison of CHAMP climate records 2002 to 2008 from six data centers

To examine the claim that Global Positioning System (GPS) radio occultation (RO) data are useful as a benchmark data set for climate monitoring, the structural uncertainties of retrieved profiles that result from different processing methods are quantified. Profile‐to‐profile comparisons of CHAMP (CHAllenging Minisatellite Payload) data from January 2002 to August 2008 retrieved by six RO processing centers are presented. Differences and standard deviations of the individual centers relative to the inter‐center mean are used to quantify the structural uncertainty. Uncertainties accumulate in derived variables due to propagation through the RO retrieval chain. This is reflected in the inter‐center differences, which are small for bending angle and refractivity increasing to dry temperature, dry pressure, and dry geopotential height. The mean differences of the time series in the 8 km to 30 km layer range from −0.08% to 0.12% for bending angle, −0.03% to 0.02% for refractivity, −0.27 K to 0.15 K for dry temperature, −0.04% to 0.04% for dry pressure, and −7.6 m to 6.8 m for dry geopotential height. The corresponding standard deviations are within 0.02%, 0.01%, 0.06 K, 0.02%, and 2.0 m, respectively. The mean trend differences from 8 km to 30 km for bending angle, refractivity, dry temperature, dry pressure, and dry geopotential height are within ±0.02%/5 yrs, ±0.02%/5 yrs, ±0.06 K/5 yrs, ±0.02%/5 yrs, and ±2.3 m/5 yrs, respectively. Although the RO‐derived variables are not readily traceable to the international system of units, the high precision nature of the raw RO observables is preserved in the inversion chain.

Mapping GPS Radio Occultation Data by Bayesian Interpolation

Abstract Bayesian interpolation for mapping GPS radio occultation data on a sphere is explored and its performance evaluated. Bayesian interpolation is ideally suited to the task of fitting data randomly and nonuniformly distributed with unknown error without overfitting the data. The geopotential height at dry pressure 200 hPa is simulated as data with theoretical distributions of the Challenging Mini-Satellite Payload (CHAMP) and of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC). The simulated CHAMP data are found to be best fit with a spherical harmonic basis of 14th degree; the COSMIC data with a spherical harmonic basis of 20th degree. The best regularizer mimics a spline fit, and relaxing the penalty for purely meridional structures or for the global mean yields little advantage. Climatologies are most accurately established by binning in ≃2-day intervals to best resolve synoptic structures in space and time. Finally, Bayesian interpolation is shown to negate a source of systematic sampling error obtained in binning and averaging highly nonuniform data but to incur another systematic error due to incomplete resolution of the background atmosphere, notably in the Southern Hemisphere.

Globally averaged exospheric temperatures derived from CHAMP and GRACE accelerometer measurements

Neutral densities (ρ) inferred from accelerometer measurements on the polar‐orbiting Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) satellites are used to compile exospheric temperatures (T∞) during extended periods in 2003 and 2004 when their orbital planes were nearly parallel and at quadrature, respectively. Exospheric temperatures were first estimated using ρ‐h‐T∞ relationships implicit within the Jacchia models, then averaged over individual orbits. We found good agreement between the orbital‐averaged T∞ obtained from CHAMP and GRACE accelerometer data as well as with globally averaged exospheric temperatures derived from drag measurements from the constellation of satellites used in the High‐Accuracy Satellite Drag Model. Our analysis corrects a critical conjecture by Burke (2008) that globally averaged T∞ is essentially the same as orbit‐averaged values obtained by polar‐orbiting satellites, independent of the local time of their orbital planes. Unlike the symmetric 0200–1400 LT distribution of T∞ minima and maxima found in early Jacchia models, presented data indicate that the minima are located closer to the dawn meridian. We also demonstrate that the averaging technique used to estimate T∞ affects the outcomes. Statistical analyses provide an empirical basis for improving estimates of the thermosphere's total energy budget.

CHAMP observations of global gravity wave fields in the troposphere and stratosphere

This paper presents the analysis of 56 months of CHAMP (Challenging Minisatellite Payload) RO (radio occultation) temperature profiles to study the global gravity wave fields in the troposphere and lower stratosphere. The relatively new GPS (Global Positioning System) RO technique is suitable for global monitoring and provides high vertical resolution, long‐term stability, and opportunity of all‐weather viewing. The CHAMP data set analyzed in this paper is the largest RO data set used for the investigation of global structure of gravity waves. The analysis also makes use of the global radiosonde data provided by the British Atmospheric Data Centre (BADC). First, the global winter temperature pattern and temperature fluctuations in the troposphere and lower stratosphere are discussed. The global structure of gravity wave variances in the upper troposphere and lower stratosphere for the summer and winter seasons indicate the dominance in the tropical region. The analysis reveals that the maximum potential energy (Ep) is observed around the equator with considerable longitudinal variation. Comparison of the wave energy derived from the CHAMP and radiosonde data sets for the low‐latitude region (20°S–20°N) shows good similarity. The observed vertical wave number power spectra were compared with a model spectrum. The low‐latitude power spectra show considerable similarities with the model spectrum. However, the disparity increases in the midlatitude or high‐latitude sectors; particularly, in the high latitudes the observed spectral power is much underestimated for longer wavelengths. The results demonstrate the usefulness of GPS RO data in determining the global gravity wave fields.

Radio holographic filtering, error estimation, and quality control of radio occultation data

Processing of radio occultation data requires filtering and quality control for the noise reduction and sorting out corrupted data samples. We introduce a radio holographic filtering algorithm based on the synthesis of canonical transform (CT2) and radio holographic focused synthesized aperture (RHFSA) methods. The field in the CT2‐transformed space is divided by a reference signal to subtract the regular phase variation and to compress the spectrum. Next, it is convolved with a Gaussian filter window and multiplied by the reference signal to restore the phase variation. This algorithm is simple to implement, and it is numerically efficient. Numerical simulation of processing radio occultations with a realistic receiver noise indicates a good performance of the method. We introduce a new technique of the error estimation of retrieved bending angle profiles based on the width of the running spectra of the transformed wavefield multiplied with the reference signal. We describe a quality control method for the discrimination of corrupted samples in the L2 channel, which is most susceptible to signal tracking errors. We apply the quality control and error estimation techniques for the processing of data acquired by Challenging Minisatellite Payload (CHAMP) and perform a statistical comparison of CHAMP data with the analyses of the German Weather Service (DWD). The statistical analysis shows a good agreement between the CHAMP and DWD error estimates and the observed CHAMP–DWD differences. This corroborates the efficiency of the proposed quality control and error estimation techniques.

A quality control procedure for GPS radio occultation data

A quality control (QC) procedure is applied to the Challenging Minisatellite Payload (CHAMP) level‐2 Global Position System (GPS) radio occultation (RO) data provided by the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archival Center (CDAAC). It consists of a range check removing negative values, a biweight check removing data which deviate from the biweight mean by more than four times the biweight standard deviation, another biweight check removing data with large deviations to the National Center for Environmental Prediction (NCEP) analysis, and finally a symmetric check removing the negative bias of GPS observations in the low troposphere below 4 km. These four QC checks are applied sequentially to identify outliers in GPS bending angle and refractivity data at each vertical level using data primarily in the month of March 2004. Having removed 5.5% outlier data, the GPS RO observations compared much more favorably with the NCEP analyses than the original data without QC, resulting in an improved spatial consistency, a more symmetric probability distribution, significantly reduced error variances, and a nearly diagonal vertical error correlation matrix. The effectiveness of the proposed QC procedure is further confirmed by showing that most RO profiles removed by the QC procedure proposed in this study have those characteristic physical parameter values indicating poor quality of RO data. Similar results are obtained when the same QC procedure is applied to CHAMP data in July 2002, which shows the robustness of the proposed GPS QC procedure.

Derivation of the CHAMP-only global gravity field model TUG-CHAMP04 applying the energy integral approach

A global gravity field model TUG-CHAMP04, derived from CHAMP (CHAllenging Minisatellite Payload) satellite-to-satellite GPS tracking observations in the high-low mode (SST-hl) in combination with CHAMP accelerometry, is presented and described in detail in this paper. For this purpose the energy integral approach was applied to precise kinematic orbits and accelerometer data. The advantage of these kinds of orbits is that they are derived from purely geometrical information, hence no external gravity field information is used for the determination of the positions. The disadvantage of precise kinematic orbit information is, that no velocities are delivered and hence a procedure has to be elaborated to deduce the velocities from kinematic positions. This work is done in preparation for ESA’s GOCE (Gravity field and steady state Ocean Circulation Explorer) satellite mission (scheduled launch November 2006), aiming at a high precision and high-resolution gravity field model on a global scale. This paper concentrates on the CHAMP data processing, where, in contrast to the usual standard method (processing in the Earth fixed frame), an approach in the inertial frame is chosen. Focus is taken on the data preprocessing of both accelerometer and orbit data, emphasising on the correct treatment of data-gaps and outlier detection. Furthermore an arc-wise weighting strategy is introduced and the advantages/disadvantages of this approach are discussed. Finally, the TUG-CHAMP04 model, calculated from one year of CHAMP data is compared with the official CHAMP gravity field model EIGEN-3p and terrestrial data (GPS levelling data).

Using CHAMP radio occultation data to determine the top altitude of the Planetary Boundary Layer

A simple approach to derive the Planetary Boundary Layer (PBL) top altitude from CHAMP (CHAllenging Minisatellite Payload) radio occultation (RO) data is presented. Our RO processing cuts off at an altitude, typically ≤4 km, below which the GPS signals are affected by tracking errors. This lowest processed altitude (LPA) is assumed to coincide with the PBL top. We average LPAs for the years 2001 to 2004 over 5 Degree latitude longitude boxes and compare them to ECMWF analysis data. The ECMWF PBL top was calculated from the relative humidity gradient with respect to altitude. Agreement between the data sets is good in terms of mean PBL height, especially over sea. The CHAMP data shows the major features of PBL height with a realistic transition from stratocumulus regions to shallow and deep cumulus areas. CHAMP also shows a substantial amount of PBL height variability that may prove useful to study PBL dynamics.

Integrated adjustment of CHAMP, GRACE, and GPS data

Various types of observations, such as space-borne Global positioning system (GPS) code and phase data, accelerometer data, K-band range and range-rate data, and ground-based satellite laser ranging data of the CHAllenging Minisatellite Payload (CHAMP) and GRAvity Climate Experiment (GRACE) satellite missions, are used together with ground-based GPS code and phase data in a rigorous adjustment to eventually solve for the ephemerides of the CHAMP, GRACE, and GPS satellites, geocenter variations, and low-degree gravity field parameters. It turns out that this ‘integrated’ adjustment considerably improves the accuracy of the ephemerides for the high and low satellites, geocenter variations, and gravity field parameters, compared to the case when the adjustment is carried out stepwise or in individual satellite solutions.

Impact of CHAMP Radio Occultation Observations on Global Analysis and Forecasts in the Absence of AMSU Radiance Data

Challenging Minisatellite Payload (CHAMP) radio occultation (RO) observations during a two-week period are assimilated into global analyses using the National Center for Environmental Prediction (NCEP) three-dimensional variational (3D-Var) system with a recently improved observation operator for assimilating GPS bending angle data. The NCEP 3D-Var system used in this research is suboptimal since Advanced Microwave Unit (AMSU) radiances are not included in our experiments. Analyses with and without CHAMP observations are compared with each other and with collocated conventional radiosonde and dropsonde data, which are excluded from both experiments. Zonal mean temperature, humidity and surface pressure differences between the GPS analyses and NO-GPS analyses are examined. The GPS analyses in the Southern Hemisphere show higher temperatures than the NO-GPS analyses, particularly in the mid- and high latitudes. The GPS analyses show drier air in the lower troposphere and more moist air in the middle troposphere compared to the NO-GPS analyses. The surface pressure is slightly increased (maximum 0.8 hPa) in the Southern Hemisphere and decreased (maximum 0.25 hPa) in the Northern Hemisphere due to the inclusion of GPS observations. Compared with the collocated independent soundings, the large cold bias (as large as 2.5 K) in the NCEP Southern Hemisphere analyses produced without CHAMP observations is significantly reduced. On average, a 20% mean error reduction in the temperature analysis is obtained in the Southern Hemisphere when CHAMP data are included. Degradations in the surface pressure analysis found from previous the GPS/Meteorology data assimilation studies are greatly reduced. The differences between the surface pressure analysis errors with and without CHAMP data are less than 0.8±1.5 hPa. Comparisons of numerical forecasts initialized with analyses produced with and without CHAMP occultations display a small improvement in the forecasts in the tropics and the Southern Hemisphere associated with the use of the CHAMP observations.

Analysis and validation of Challenging Minisatellite Payload (CHAMP) radio occultation data

We performed statistical comparisons of data from Challenging Minisatellite Payload (CHAMP) with European Centre for Medium‐Range Weather Forecasting (ECMWF) analyses. Our data processing technique includes: (1) radio holographic (radio optics) and canonical transform analysis of wave fields, (2) visualization of the local spatial spectra of wave fields for interactive data quality control, (3) ionospheric correction and noise reduction based on statistical optimization, (4) Abel inversion, and (5) comparison of CHAMP and ECMWF data based on forward modeling and inversion of artificial radio occultation data. In the tropics and midlatitudes at heights below 10 km, the CHAMP‐ECMWF differences are significantly greater than the GPS Meteorology (GPS/MET)‐ECMWF differences. Because big systematic negative differences between observed and simulated refraction angles are also observed in this region, we can explain that by poor quality of the CHAMP data in multipath regions due to signal tracking errors. Elsewhere, systematic CHAMP‐ECMWF differences do not exceed 1 K in the tropics and polar latitudes, and 0.5 K in midlatitudes, and deviations of CHAMP data from ECMWF analyses are smaller than those of GPS/MET data. This can be explained by enhancement in the latest ECMWF analyses.

A Practical Algorithm for LEO Orbit Determination

: The primary objective of this paper is to present a practical, fast, and effective triple-difference algorithm for precision orbit determination (POD) of low earth orbiters (LEOs). This technique was developed for near-real-time POD to support occultation data processing, as well as atmospheric data retrieval from the land-based GPS network. In particular, the paper focuses on the GPS/LEO POD, with special emphasis on the triple-difference–based kinematic approach to LEO POD. The paper provides the theoretical foundation of the method, and presents the results of a performance analysis for the CHAMP (CHAllenging Minisatellite Payload) satellite POD. In addition, data screening and quality control procedures applied to CHAMP and ground station GPS data are presented, with preliminary statistics related to the number and distribution of cycle-slip events. A comparison of the results from two cycle-slip detection methods is also included.