Error analysis for mesospheric temperature profiling by absorptive occultation sensors

International audienceAn error analysis for mesospheric profiles retrieved from absorptive occultation data has been performed, starting with realistic error assumptions as would apply to intensity data collected by available high-precision UV photodiode sensors. Propagation of statistical errors was investigated through the complete retrieval chain from measured intensity profiles to atmospheric density, pressure, and temperature profiles. We assumed unbiased errors as the occultation method is essentially self-calibrating and straight-line propagation of occulted signals as we focus on heights of 50?100 km, where refractive bending of the sensed radiation is negligible. Throughout the analysis the errors were characterized at each retrieval step by their mean profile, their covariance matrix and their probability density function (pdf). This furnishes, compared to a variance-only estimation, a much improved insight into the error propagation mechanism. We applied the procedure to a baseline analysis of the performance of a recently proposed solar UV occultation sensor (SMAS ? Sun Monitor and Atmospheric Sounder) and provide, using a reasonable exponential atmospheric model as background, results on error standard deviations and error correlation functions of density, pressure, and temperature profiles. Two different sensor photodiode assumptions are discussed, respectively, diamond diodes (DD) with 0.03% and silicon diodes (SD) with 0.1% (unattenuated intensity) measurement noise at 10 Hz sampling rate. A factor-of-2 margin was applied to these noise values in order to roughly account for unmodeled cross section uncertainties. Within the entire height domain (50?100 km) we find temperature to be retrieved to better than 0.3 K (DD) / 1 K (SD) accuracy, respectively, at 2 km height resolution. The results indicate that absorptive occultations acquired by a SMAS-type sensor could provide mesospheric profiles of fundamental variables such as temperature with unprecedented accuracy and vertical resolution. A major part of the error analysis also applies to refractive (e.g., Global Navigation Satellite System based) occultations as well as to any temperature profile retrieval based on air density or major species density measurements (e.g., from Rayleigh lidar or falling sphere techniques)

Errors in GNSS radio occultation data: relevance of the measurement geometry and obliquity of profiles

Atmospheric profiles retrieved from GNSS (Global Navigation Satellite System) radio occultation (RO) measurements are increasingly used to validate other measurement data. For this purpose it is important to be aware of the characteristics of RO measurements. RO data are frequently compared with vertical reference profiles, but the RO method does not provide vertical scans through the atmosphere. The average elevation angle of the tangent point trajectory (which would be 90° for a vertical scan) is about 40° at altitudes above 70 km, decreasing to about 25° at 20 km and to less than 5° below 3 km. In an atmosphere with high horizontal variability we can thus expect noticeable representativeness errors if the retrieved profiles are compared with vertical reference profiles. We have performed an end-to-end simulation study using high-resolution analysis fields (T799L91) from the European Centre for Medium-Range Weather Forecasts (ECMWF) to simulate a representative ensemble of RO profiles via high-precision 3-D ray tracing. Thereby we focused on the dependence of systematic and random errors on the measurement geometry, specifically on the incidence angle of the RO measurement rays with respect to the orbit plane of the receiving satellite, also termed azimuth angle, which determines the obliquity of RO profiles. We analyzed by how much errors are reduced if the reference profile is not taken vertical at the mean tangent point but along the retrieved tangent point trajectory (TPT) of the RO profile. The exact TPT can only be determined by performing ray tracing, but our results confirm that the retrieved TPT – calculated from observed impact parameters – is a very good approximation to the "true" one. Systematic and random errors in RO data increase with increasing azimuth angle, less if the TPT is properly taken in to account, since the increasing obliquity of the RO profiles leads to an increasing sensitivity to departures from horizontal symmetry. Up to an azimuth angle of 30°, however, this effect is small, even if the RO profiles are assumed to be vertical. For applications requiring highest accuracy and precision it is advisable to exclude RO profiles with ray incidence angles beyond an azimuth of 50°. Errors in retrieved atmospheric profiles decrease significantly, by up to a factor of 2, if the RO data are exploited along the retrieved TPT. The tangent point trajectory of RO profiles should therefore be exploited whenever this is possible

Retrieval of temperature profiles from CHAMP for climate monitoring: intercomparison with Envisat MIPAS and GOMOS and different atmospheric analyses

International audienceThis study describes and evaluates a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval scheme particularly aimed at delivering bias-free atmospheric parameters for climate monitoring and research. The focus of the retrieval is on the sensible use of a priori information for careful high-altitude initialisation in order to maximise the usable altitude range. The RO retrieval scheme has been meanwhile applied to more than five years of data (September 2001 to present) from the German CHAllenging Minisatellite Payload for geoscientific research (CHAMP) satellite. In this study it was validated against various correlative datasets including the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Global Ozone Monitoring for Occultation of Stars (GOMOS) sensors on Envisat, five different atmospheric analyses, and the operational CHAMP retrieval product from GeoForschungsZentrum (GFZ) Potsdam. In the global mean within 10 to 30 km altitude we find that the present validation observationally constrains the potential RO temperature bias to be <0.2 K. Latitudinally resolved analyses show biases to be observationally constrained to <0.2?0.5 K up to 35 km in most cases, and up to 30 km in any case, even if severely biased (about 10 K or more) a priori information is used in the high altitude initialisation of the retrieval. No evidence is found for the 10?35 km altitude range of residual RO bias sources other than those potentially propagated downward from initialisation, indicating that the widely quoted RO promise of "unbiasedness and long-term stability due to intrinsic self-calibration" can indeed be realised given care in the data processing to strictly limit structural uncertainty. The results thus reinforce that adequate high-altitude initialisation is crucial for accurate stratospheric RO retrievals. The common method of initialising, at some altitude in the upper stratosphere, the hydrostatic integral with an upper boundary temperature or pressure value derived from meteorological analyses is prone to introduce biases from the upper boundary down to below 25 km. Also above 30 to 35 km, GNSS RO delivers a considerable amount of observed information up to around 40 km, which is particularly interesting for numerical weather prediction (NWP) systems, where direct assimilation of non-initialised observed RO bending angles (free of a priori) is thus the method of choice. The results underline the value of RO for climate applications

Small catchment runoff sensitivity to station density and spatial interpolation: Hydrological modeling of heavy rainfall using a dense rain Gauge network

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km2) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km2) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance &lt; 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively

Where to see climate change best in radio occultation variables – study using GCMs and ECMWF reanalyses

Radio occultation (RO) is a new technique to observe the upper troposphere and lower stratosphere (UTLS), a region that reacts particularly sensitive to climate change. Featuring characteristics such as long-term stability, SI traceability, all-weather capability, global coverage, and high accuracy and vertical resolution, RO data fulfill the requirements for climate monitoring in the UTLS. However, while a range of studies has shown the climate utility of RO it has not yet been explored sytematically where to see climate change best in RO variables. Therefore we perform here a systematic trend study for the RO variables refractivity, pressure, and temperature (bending angle, not depending on height but impact parameter, is left for separate study). The trends, given at geopotential height levels and for layer gradients, are explored to determine seasons, geographic regions, and height domains, which show a significant trend signal. Because continuous RO data are available since 2001 only, reanalyses (ERA-40 and ERA-Interim) and global circulation model simulations of the Intergovernmental Panel on Climate Change Assessment Report 4 (CCSM3, ECHAM5, HadCM3) are used as proxy data for RO. It is shown that RO data are sensitive at different height ranges and that thus several indicators of climate change can be retrieved. Refractivity emerges as indicator in the lower stratosphere (LS) and tropopause region at about 14 km to 24 km, pressure over the whole UTLS, and both in all large-scale regions except the polar caps. Temperature qualifies as indicator in the upper troposphere below about 16 km and in the LS above about 21 km. Overall, refractivity and pressure alone are adequate indicators for the UTLS, but temperature as commonly used variable facilitates easy interpretation of results. Layer gradients were found to be further sensitive indicators providing additional information. Besides large-scale global and hemispheric means the tropics and the mid-latitudes appear as regions suitable to track climate change with RO data. The results also point to the value of utilizing in addition to annual means specific seasons, such as northern hemispheric fall and summer, for early climate signal detection. Since RO data feature much better vertical resolution than the proxy data of this study, more detailed insights can be expected when a longer RO record will be available

Retrieval of temperature profiles from CHAMP for climate monitoring: intercomparison with Envisat MIPAS and GOMOS and different atmospheric analyses

This study describes and evaluates a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval scheme particularly aimed at delivering bias-free atmospheric parameters for climate monitoring and research. The focus of the retrieval is on the sensible use of a priori information for careful high-altitude initialisation in order to maximise the usable altitude range. The RO retrieval scheme has been meanwhile applied to more than five years of data (September 2001 to present) from the German CHAllenging Minisatellite Payload for geoscientific research (CHAMP) satellite. In this study it was validated against various correlative datasets including the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Global Ozone Monitoring for Occultation of Stars (GOMOS) sensors on Envisat, five different atmospheric analyses, and the operational CHAMP retrieval product from GeoForschungsZentrum (GFZ) Potsdam. In the global mean within 10 to 30 km altitude we find that the present validation observationally constrains the potential RO temperature bias to be <0.2 K. Latitudinally resolved analyses show biases to be observationally constrained to <0.2–0.5K up to 35 km in most cases, and up to 30 km in any case, even if severely biased (about 10K or more) a priori information is used in the high altitude initialisation of the retrieval. No evidence is found for the 10–35 km altitude range of residual RO bias sources other than those potentially propagated downward from initialisation, indicating that the widely quoted RO promise of “unbiasedness and long-term stability due to intrinsic self-calibration” can indeed be realised given care in the data processing to strictly limit structural uncertainty. The results thus reinforce that adequate high-altitude initialisation is crucial for accurate stratospheric RO retrievals. The common method of initialising, at some altitude in the upper stratosphere, the hydrostatic integral with an upper boundary temperature or pressure value derived from meteorological analyses is prone to introduce biases from the upper boundary down to below 25 km. Also above 30 to 35 km, GNSS RO delivers a considerable amount of observed information up to around 40 km, which is particularly interesting for numerical weather prediction (NWP) systems, where direct assimilation of non-initialised observed RO bending angles (free of a priori) is thus the method of choice. The results underline the value of RO for climate applications