Assimilation Of Radio Occultation Data Research Articles

Research on the implementation of GPS radio occultation one-dimensional bending angle operator in four-dimensional variational data assimilation system

GPS radio occultation data are implemented in the numerical weather prediction as a kind of very important observation data because of its excellent characteristics, which makes a remarkable positive effect in the system. In this article, we design an implement method and a quality control strategy of one-dimensional bending angle operator in four-dimensional variational data assimilation system, and we also describe the steps of implementing one-dimensional bending angle forward operator and its linear and adjoint operators in four-dimensional variational data assimilation system. The author chose Reger’s three term expression and its best average coefficients are chosen to calculate refractivity values. In this article, we introduce an observation error model of one-dimensional bending angle operator through the statistics of GPS radio occultation observation data of August, 2012. A comparison of experiment of the analysis field between in the cases with and without GPS radio occultation data is made. It is concluded that GPS radio occultation data plays a positive role in the global areas, especially in the low and middle latitude areas higher than 10 km. This conclusion is the foundation of the following-up GPS radio occultation data assimilation verification experiments.

(Near-)real-time orbit determination for GNSS radio occultation processing

The processing of GPS radio occultation measurements for use in numerical weather predictions requires a precise orbit determination (POD) of the host satellite in near-real-time. Making use of data from the GRAS instrument on Metop-A, the performance of different GPS ephemeris products and processing concepts for near-real-time and real-time POD is compared. While previous analyses have focused on the achievable along-track velocity accuracy, this study contributes a systematic comparison of the resulting estimated bending angles. This enables a more rigorous trade-off of different orbit determination methodologies in relation to the end-user needs for atmospheric science products. It is demonstrated that near-real-time GPS orbit and clock products have reached a sufficient quality to determine the Metop-A along-track velocity with an accuracy of better than 0.05 mm/s that was formerly only accessible in post-processing. The resulting bending angles are shown to exhibit standard deviation and bias differences of less than 0.3 % compared with post-processed products up to altitudes of at least 40 km, which is notably better than 1 % accuracy typically assumed for numerical weather predictions in this height regime. Complementary to the analysis of ground-based processing schemes, the potential of autonomous on-board orbit determination is investigated for the first time. Using actual GRAS flight data, it is shown that a 0.5 m 3D rms position accuracy and a 0.2 mm/s along-track velocity accuracy can in fact be obtained in real-time with the currently available GPS broadcast ephemeris quality. Bending angles derived from the simulated real-time processing exhibit a minor performance degradation above tangent point heights of 40 km but negligible differences with respect to ground-based products below this altitude. Onboard orbit determination and, if desired, bending angle computation, can thus enable a further simplification of the ground segment in future radio occultation missions and contribute to reduced product latencies for radio occultation data assimilation in numerical weather predictions.

Data assimilation retrieval of electron density profiles from radio occultation measurements

[1] In this paper, the Kalman filter is used to retrieve the electron density profile along the tangent points by assimilating the slant total electron content data observed during a radio occultation (RO) event into an empirical background model. The RO data observed by COSMIC satellites on day of year 266 in 2009 are selected to do both the simulation work and the real data retrieval test. The results show that the data assimilation technique can improve the electron density retrieval in comparison with the Abel inversion. It is less influenced by the ionospheric inhomogeneity than the Abel method. Some pseudo‐large‐scale features made by the Abel retrieval, such as the plasma cave underneath the equatorial ionization anomaly region and the three peaks along the latitude direction in the E layer, disappear in the data assimilation retrieval results. Independent validation by ground‐based ionosonde observations confirms the improvement of data assimilation retrieval below the F2 peak. In addition, some potential research on RO data assimilation is also discussed.

Impact of GPS radio occultation data assimilation on regional weather predictions

The impact of GPS radio occultation (RO) data assimilation on severe weather predictions in East Asia is introduced and reviewed. Both the local observation operator that assimilates the retrieved refractivity as local point measurement, and the nonlocal observation operator that assimilates the integrated retrieved refractivity along a straight raypath have been utilized in WRF 3DVAR to improve the initial analysis of the model. A general evaluation of the impact of these approaches on Asian regional analysis and daily prediction is provided in this paper. In general, the GPS RO data assimilation may improve prediction of severe weather such as typhoons and Mei-yu systems when COSMIC data were available, ranging from several points in 2006 to a maximum of about 60 in 2007 and 2008 in this region. Based on a number of experiments, regional model predictions at 5 km resolution were not significantly influenced by different observation operators, although the nonlocal observation operator sometimes results in slightly better track forecast. These positive impacts are seen not only in typhoon track prediction but also in prediction of local heavy rainfall associated with severe weather over Taiwan. The impact of 56 GPS RO soundings on track prediction of Cyclone Gonu (2007) over the Indian Ocean is also appealing when compared to other tracks assimilated with different observations. From a successive evaluation of skill scores for real-time forecasts on Mei-yu frontal systems operationally conducted over a longer period and predictions of six typhoons in 2008, assimilation of GPS RO data appears to have some positive impact on regional weather predictions, on top of existent assimilation with all other observations.

Test of a non-local excess phase delay operator for GPS radio occultation data assimilation

A physically-sound, non-local excess phase delay observation operator is developed for simulating excess phase delay measurements from GPS radio occultation (RO) missions. By approximating an observed ray by a straight line, the refractivity gradient information along an observed ray path is included in the simulated excess phase delay. This observation operator is used to simulate observations from the German CHAllenging Minisatellite Payload (CHAMP) RO mission based on large-scale analysis. The need to use such an observation operator for GPS RO data assimilation in spherically asymmetric regions is shown by results from a set of forward simulation and data assimilation experiments. A modification that renders the non-local excess phase delay observation operator more suitable for parallel implementation of GPS RO data assimilation is proposed.

Variational data assimilation for deriving global climate analyses from GNSS radio occultation data

A comprehensive global navigation satellite system (GNSS) based radio occultation (RO) data set is available for meteorology and climate applications since the start of GNSS RO measurements aboard the CHAllenging Mini-satellite Payload (CHAMP) satellite in February 2001. Global coverage, all-weather capability, long-term stability and accuracy not only makes this innovative use of GNSS signals a valuable supplement to the data set assimilated into numerical weather prediction (NWP) systems but also an excellent candidate for global climate monitoring. We present a 3D variational data assimilation (3D-Var) scheme developed to derive consistent global analysis fields of temperature, specific humidity, and surface pressure from GNSS RO data. The system is based on the assimilation of RO data within 6 h time windows into European Centre for Medium-Range Weather Forecasts (ECMWF) short-term (24 h, 30 h) forecasts, to derive climatologic monthly mean fields. July 2003 was used as a test-bed for assessing the system’s performance. The results show good agreement with climatologies derived from RO data only and recent NWP impact studies. These findings are encouraging for future developments to apply the approach for longer term climatologic analyses, validation of other data sets, and atmospheric variability studies.

The Assimilation of GPS Radio Occultation Data and its Impact on Rainfall Prediction along the West Coast of India during Monsoon 2002

In this study, the Weather Research and Forecasting (WRF-2.0.3.1) model with three-dimensional variational data assimilation (3DVAR) was utilized to study a heavy rainfall event along the west coast of India with and without the assimilation of GPS occultation refractivity soundings in the monsoon period of 2002. The WRF model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research communities. The Global Positioning System (GPS) radio occultation (RO) refractivity data, processed by UCAR, were obtained from the CHAMP and SAC-C missions. This study investigates the impact of thirteen GPS occultation refractivity soundings only, as assimilated into the WRF model with 3DVAR, on the rainfall prediction over the western coastal mountain of India. The model simulation, with the finest resolution of 10 km, was in good agreement with rainfall observations, up to 72-h forecast. There are some subtle but important differences in predicted rainfalls between the control run CN (without the assimilation of refractivity soundings) and G13 (with the assimilation of thirteen GPS RO soundings). In general, the assimilation run G13 gives a better prediction in terms of both rainfall locations and amounts at later times. The moisture increments were analyzed at the initial and forecast times to assess the impact of GPS RO data assimilation. The results indicate that remote soundings in the forcing region could have significant impacts on distant downstream regions. It is anticipated, based on this study, that considerably occultation soundings available from the six-satellite constellation of FORMOSAT-3/COSMIC would have even more significant impacts on weather prediction in this region.

Importance of Forecast Error Multivariate Correlations in Idealized Assimilations of GPS Radio Occultation Data with the Ensemble Adjustment Filter

Abstract The importance of multivariate forecast error correlations between specific humidity, temperature, and surface pressure in perfect model assimilations of Global Positioning System radio occultation (RO) refractivity data is examined using the Ensemble Adjustment Filter (EAF) and the NCAR global Community Atmospheric Model, version 3. The goal is to explore whether inclusion of the multivariate forecast error correlations in the background term of 3D and 4D variational data assimilation systems (3DVAR and 4DVAR, respectively) is likely to improve RO data assimilation in the troposphere. It is not possible to explicitly neglect multivariate forecast error correlations with the EAF because they are not used directly in the algorithm. Instead, the filter only makes use of the forecast error correlations between observed quantities (RO here) and model state variables. However, because the forecast error correlations for RO observations are dominated by correlations with a subset of state variable types in certain regions, the importance of multivariate forecast error correlations between state variables can be indirectly assessed. This is done by setting the forecast error correlations of RO observations and some state variables (e.g., temperature) to zero in a set of assimilation experiments. Comparing these experiments to a control in which all state variables are impacted by RO observations allows an indirect assessment of the importance of multivariate correlations between state variables not impacted by the observations and those that are impacted. Results suggest that proper specification of the multivariate forecast error correlations in 3DVAR and 4DVAR systems should improve the analysis of specific humidity, surface pressure, and temperature in the troposphere when assimilating RO data.

Evaluation of a Linear Phase Observation Operator with CHAMP Radio Occultation Data and High-Resolution Regional Analysis

Abstract In this study a nonlocal, linear observation operator for assimilating radio occultation data is evaluated. The operator consists of modeling the excess phase, that is, integrating the refractivity along straight lines tangent to rays, below a certain height. The corresponding observable is the excess phase integrated through the Abel-retrieved refractivity, along the same lines, below the same height. The operator allows very simple implementation (computationally efficient) while accurately accounting for the horizontal refractivity gradients. This is due to significant cancellation of the linearization and discretization errors when modeling the observable. Evaluation of the operator with Challenging Minisatellite Payload (CHAMP) radio occultation data and grid refractivity fields from high-resolution regional analysis over the continental United States showed reduction of the observation error in the troposphere (below 7 km) 1.5–2 times, compared to the error of local refractivity. The operator is useful for the assimilation of radio occultation data by high-resolution weather models in the troposphere.

Assessing the Accuracy of a Linearized Observation Operator for Assimilation of Radio Occultation Data: Case Simulations with a High-Resolution Weather Model

Abstract Assimilation into numerical weather models of the refractivity, Abel-retrieved from radio occultations, as the local refractivity at ray tangent point may result in large errors in the presence of strong horizontal gradients (atmospheric fronts, strong convection). To reduce these errors, other authors suggested modeling the Abel-retrieved refractivity as a nonlocal linear function of the 3D refractivity, which can be used as a linear observation operator for assimiliation. The authors of this study introduce their approach for the nonlocal linear observation operator, which consists of modeling the excess phase path, calculated along certain trajectories below the top of an atmospheric model. In this study (not aimed at development of an observation operator for any specific atmospheric model), both approaches are validated by assessing the accuracy of both linearized observation operators by numerical simulations with the high-resolution Weather Research and Forecasting (WRF) model and comparing them to the accuracy of interpretation of the Abel-retrieved refractivity as local. Improvement of the accuracy of about an order of magnitude is found with the nonlocal refractivity and further improvement is found with the excess phase path. The effect of horizontal resolution of an atmospheric model on the accuracy of modeling local and nonlocal linear observables is also investigated, and it is demonstrated that the nonlocal linear modeling of radio occultation observables is especially important for weather prediction models with sufficiently high horizontal resolution, grid size <100 km (mesoscale models).

Radio occultation bending angle and impact parameter errors caused by horizontal refractive index gradients in the troposphere: A simulation study

Radio occultation (RO) bending angle and impact parameter values are derived from a Doppler shift measurement, assuming spherical symmetry. The purpose of this work is to illustrate the errors that arise when this assumption is not valid. Doppler shift values have been simulated for ray paths through a three‐dimensional refractive index field derived from a mesoscale model forecast, which has a horizontal grid of 12 km by 12 km, and includes water vapor. These have then been inverted, making the spherical symmetry assumption. It is demonstrated that refractive index gradients perpendicular to the ray path can cause errors in both the bending angle and impact parameter values, but the latter is the more significant. It is shown that the impact parameter value at the tangent point can differ by around ∼100 m from the derived value. This can cause an effective bending angle error exceeding ∼10% near the surface. A statistical analysis of the errors caused by horizontal gradients for simulations through 54 mesoscale forecasts, using fixed spacecraft trajectories and tangent point locations, is presented. In general, the bending angle errors are found to be ∼3% near the surface. A new set of analytical expressions for errors has been derived. These are based on integrating the horizontal gradients along the ray path and are found to be in good agreement with the simulation results. The implications of this work for the assimilation of RO data into numerical weather prediction models are discussed and areas of future work are outlined.