UTLS temperature validation of MPI-ESM decadal hindcast experiments with GPS radio occultations

Global Positioning System (GPS) radio occultation (RO) temperature data are used to validate MPI-ESM (Max Planck Institute – Earth System Model) decadal hindcast experiments in the upper troposphere and lower stratosphere (UTLS) region between 300 hPa and 10 hPa (8 km and 32 km) for the time period between 2002 and 2011. The GPSRO dataset is unique since it is very precise, calibration independent and covers the globe better than the usual radiosonde dataset. In addition it is vertically finer resolved than any of the existing satellite temperature measurements available for the UTLS and provides now a unique one decade long temperature validation dataset. The initialization of the MPI-ESM decadal hindcast runs mostly increases the skill of the atmospheric temperatures when compared to uninitialized climate projections with very high skill scores for lead-year one, and gradually decreases for the later lead-years. A comparison between two different initialization sets (b0, b1) of the low-resolution (LR) MPI-ESM shows increased skills in b1-LR in most parts of the UTLS in particular in the tropics. The medium resolution (MR) MPI-ESM initializations are characterized by reduced temperature biases in the uninitialized runs as compared to observations and a better capturing of the high latitude northern hemisphere interannual polar vortex variability as compared to the LR model version. Negative skills are found for the b1-MR hindcasts however in the regions around the mid-latitude tropospheric jets on both hemispheres and in the vicinity of the tropical tropopause in comparison to the b1-LR variant. It is interesting to highlight that none of the model experiments can reproduce the observed positive temperature trend in the tropical tropopause region since 2001 as seen by GPSRO data

Global temperature estimates in the troposphere and stratosphere: a validation study of COSMIC/FORMOSAT-3 measurements

This paper mainly focuses on the validation of temperature estimates derived with the newly launched Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC)/Formosa Satellite 3 (FORMOSAT-3) system. The analysis is based on the radio occultation (RO) data samples collected during the first year observation from April 2006 to April 2007. For the validation, we have used the operational stratospheric analyses including the National Centers for Environmental Prediction - Reanalysis (NCEP), the Japanese 25-year Reanalysis (JRA-25), and the United Kingdom Met Office (MetO) data sets. Comparisons done in different formats reveal good agreement between the COSMIC and reanalysis outputs. Spatially, the largest deviations are noted in the polar latitudes, and height-wise, the tropical tropopause region noted the maximum differences (2–4 K). We found that among the three reanalysis data sets the NCEP data sets have the best resemblance with the COSMIC measurements

UTLS temperature validation of MPI-ESM decadal hindcast experiments with GPS radio occultations

Global Positioning System (GPS) radio occultation (RO) temperature data are used to validate MPI-ESM (Max Planck Institute – Earth System Model) decadal hindcast experiments in the upper troposphere and lower stratosphere (UTLS) region between 300 hPa and 10 hPa (8 km and 32 km) for the time period between 2002 and 2011. The GPSRO dataset is unique since it is very precise, calibration independent and covers the globe better than the usual radiosonde dataset. In addition it is vertically finer resolved than any of the existing satellite temperature measurements available for the UTLS and provides now a unique one decade long temperature validation dataset. The initialization of the MPI-ESM decadal hindcast runs mostly increases the skill of the atmospheric temperatures when compared to uninitialized climate projections with very high skill scores for lead-year one, and gradually decreases for the later lead-years. A comparison between two different initialization sets (b0, b1) of the low-resolution (LR) MPI-ESM shows increased skills in b1-LR in most parts of the UTLS in particular in the tropics. The medium resolution (MR) MPI-ESM initializations are characterized by reduced temperature biases in the uninitialized runs as compared to observations and a better capturing of the high latitude northern hemisphere interannual polar vortex variability as compared to the LR model version. Negative skills are found for the b1-MR hindcasts however in the regions around the mid-latitude tropospheric jets on both hemispheres and in the vicinity of the tropical tropopause in comparison to the b1-LR variant. It is interesting to highlight that none of the model experiments can reproduce the observed positive temperature trend in the tropical tropopause region since 2001 as seen by GPSRO data

Radio Occultation Method for Remote Sensing of the Atmosphere and Ionosphere

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Refractivity and temperature climate records from multiple radio occultation satellites consistent within 0.05%

Data consistency is an important prerequisite to build radio occultation (RO) climatologies based on a combined record of data from different satellites. The presence of multiple RO receiving satellites in orbit over the same time period allows for testing this consistency. We used RO data from CHAMP (CHAllenging Minisatellite Payload for geoscientific research), six FORMOSAT-3/COSMIC satellites (Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate, F3C), and GRACE-A (Gravity Recovery and Climate Experiment). We show latitude-altitude-resolved results for an example month (October 2007) and the temporal evolution of differences in a climate record of global and monthly means from January 2007 to December 2009. Latitude- and altitude-resolved refractivity and dry temperature climatologies clearly show the influence of different sampling characteristics; monthly mean deviations from the multi-satellite mean over the altitude domain 10 km to 30 km typically reach 0.1% and 0.2 K, respectively. Nevertheless, the 3-yr average deviations (shorter for CHAMP) are less than 0.03% and 0.05 K, respectively. We find no indications for instrument degradation, temporal inhomogeneities in the RO records, or temporal trends in sampling patterns. Based on analysis fields from ECMWF (European Centre for Medium-Range Weather Forecasts), we can estimate – and subtract – the sampling error from each monthly climatology. After such subtraction, refractivity deviations are found reduced to <0.05% in almost any month and dry temperature deviations to <0.05 K (<0.02% relative) for almost every satellite and month. 3-yr average deviations are even reduced to <0.01% and <0.01 K (CHAMP: −0.05 K), respectively, establishing an amazing consistency of RO climatologies from different satellites. If applying the same processing scheme for all data, refractivity and dry temperature records from individual satellites with similar bending angle noise can be safely combined up to 30 km altitude (refractivity also up to 35 km) to a consistent single climate record of substantial value for climate monitoring in the upper troposphere and lower stratosphere

Analysis of vertical wave number spectrum of atmospheric gravity waves in the stratosphere using COSMIC GPS radio occultation data

Discussion Paper;Atmos. Meas. Tech. Discuss., 4, 2071-2097, 04 Apr 2011, DOI:10.5194/amtd-4-2071-2011GPS radio occultation (RO) is characterized by high accuracy and excellent height resolution, which has great advantages in analyzing atmospheric structures including small-scale vertical fluctuations. The vertical resolution of the geometrical optics (GO) method in the stratosphere is about 1.5 km due to Fresnel radius limitations, but full spectrum inversion (FSI) can provide superior resolutions. We applied FSI to COSMIC GPS-RO profiles from ground level up to 30 km altitude, although basic retrieval at UCAR/CDAAC sets the sewing height from GO to FSI below the tropopause. We validated FSI temperature profiles with routine high-resolution radiosonde data in Malaysia and North America collected within 400 km and about 30 min of the GPS RO events. The average discrepancy at 10–30 km altitude was less than 0.5 K, and the bias was equivalent with the GO results. Using the FSI results, we analyzed the vertical wave number spectrum of normalized temperature fluctuations in the stratosphere at 20–30 km altitude, which exhibits good consistency with the model spectra of saturated gravity waves. We investigated the white noise floor that tends to appear at high wave numbers, and the substantial vertical resolution of the FSI method was estimated as about 100–200 m in the lower stratosphere. We also examined a criterion for the upper limit of the FSI profiles, beyond which bending angle perturbations due to system noises, etc, could exceed atmospheric excess phase fluctuations. We found that the FSI profiles can be used up to about 28 km in studies of temperature fluctuations with vertical wave lengths as short as 0.5 km

A new dynamic approach for statistical optimisation of GNSS radio occultation bending angles

Climate change has become a serious issue for our society. It is of great importance to accurately monitor climate change and provide reliable information to the society so that proper actions can be taken to alleviate the significant change of climate. Global Navigation Satellite Systems (GNSS) based radio occultation (RO) is a new satellite remote sensing technique that can provide high vertical resolution, long-term stable and global coverage atmospheric profiles of the Earth’s atmosphere. However, the quality of the retrieved atmospheric profiles decreases above about 30 km due to a low signal-to-noise ratio of GNSS signals at these high altitudes, since errors in bending angle profiles are propagated to refractivity profiles through an Abel integral and subsequently propagated to other atmospheric profiles through the hydrostatic integral. It is therefore important to carefully initialise the bending angles at high altitudes to minimise these error propagation effects and thereby optimise the climate monitoring utility of the retrieved profiles. Statistical optimisation is a commonly used method for this purpose. This method combines the observed bending angle profile and background bending angle profile based on their error covariance matrices to determine “optimised” bending angle profile. The focus of this thesis is to investigate an advanced statistical optimisation algorithm, which dynamically estimates both background and observation error covariance matrices, for the best determination of RO optimised bending angle profile. In this new algorithm, background bending angle profiles and their associated error covariance matrices are estimated using bending angles from multiple days of the European Centre for Medium-range Weather Forecasts (ECMWF) short-term (24h) forecast and analysis fields as well as the averaged observed bending angle. The background error matrices are constructed with geographically varying background error estimates on a daily-updated basis. The observation error covariance matrices are estimated using multiple days of RO data with geographically varying observation errors for an occultation event. The most distinctive advantage of the new algorithm is that both background and observation error covariance matrices are realistically estimated using large ensemble of climatological and observed data, while existing algorithms use crude formulations to estimate both error matrices. The new algorithm developed is evaluated against the algorithm used by the Wegener Center Occultation Processing System version 5.4 (OPSv5.4) by calculating statistical errors of retrieved atmospheric profiles relative to their reference profiles. Since the background errors at different heights are highly correlated and their covariance matrix is critical for the resulting optimised bending angles, the dynamically estimated background error covariance matrix is first used in statistical optimisation to retrieve atmospheric profiles from simulated MetOp as well as observed CHAMP and COSMIC RO events on single days. The dynamically estimated observation error covariance matrix is then used in the statistical optimisation together with the estimated background error covariance matrix to retrieve atmospheric profiles using the same test data. It can be concluded from the evaluation that if the estimated background error covariance matrix is solely used for the statistical optimisation, it can significantly reduce random errors and generate less or similar residual systematic errors (biases) in the optimised bending angles. The subsequent refractivity profiles and atmospheric (dry temperature) profiles retrieved are benefitted from the improved error characteristics of bending angles. If both observation and background error covariance matrices estimated from the new approach are used, the standard deviations of the optimised bending angles are only further reduced for simulated MetOp data, while for the observed CHAMP and COSMIC data, large random errors of bending angles are found at higher altitudes (e.g. > 50 km). This is likely to be that the observation errors are underestimated at high altitudes, where bending angles are largely affected by ionospheric effects and observation errors, and more weights are given to the noisy observed bending angles in the estimation of the optimised bending angles. Errors in CHAMP and COSMIC observed bending angles are further transferred downwards to their subsequently retrieved refractivity and dry temperature profiles, the quality of which is also degraded. The effects of the estimated background and observation error correlations on the atmospheric retrievals are investigated using simulated MetOp data. It is found that these realistically estimated correlations alone can reduce the random errors of the optimised bending angles significantly and improve the quality of the subsequent refractivities and temperatures. The performance of the new approach that uses only the new background matrix in the statistical optimisation on monthly occultation data is evaluated. The results show that the monthly errors are similar to those from single days, but in a smoother manner

A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

Variability of temperature and ozone in the upper troposphere and lower stratosphere from multi-satellite observations and reanalysis data

Temperature and ozone changes in the upper troposphere and lower stratosphere (UTLS) are important components of climate change. In this paper, variability and trends of temperature and ozone in the UTLS are investigated for the period 2002–2017 using high-quality, high vertical resolution Global Navigation Satellite System radio occultation (GNSS RO) data and improved merged satellite data sets. As part of the Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP), three reanalysis data sets, including the ERA-I, MERRA2 and the recently released ERA5, are evaluated for their representation of temperature and ozone in the UTLS. The recent temperature and ozone trends are updated with a multiple linear regression (MLR) method and related to sea surface temperature (SST) changes based on model simulations made with NCAR's Whole Atmosphere Community Climate Model (WACCM). All reanalysis temperatures show good agreement with the GNSS RO measurements in both absolute value and annual cycle. Interannual variations in temperature related to Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) processes are well represented by all reanalyses. However, evident biases can be seen in reanalyses for the linear trends of temperature since they are affected by discontinuities in assimilated observations and methods. Such biases can be corrected and the estimated trends can be significantly improved. ERA5 is significantly improved compared to ERA-I and shows the best agreement with the GNSS RO temperature. The MLR results indicate a significant warming of 0.2–0.3&thinsp;K per decade in most areas of the troposphere, with a stronger increase of 0.4–0.5&thinsp;K per decade at midlatitudes of both hemispheres. In contrast, the stratospheric temperature decreases at a rate of 0.1–0.3&thinsp;K per decade, which is most significant in the Southern Hemisphere (SH). Positive temperature trends of 0.1–0.3&thinsp;K per decade are seen in the tropical lower stratosphere (100–50&thinsp;hPa). Negative trends of ozone are found in the Northern Hemisphere (NH) at 150–50&thinsp;hPa, while positive trends are evident in the tropical lower stratosphere. Asymmetric trends of ozone can be found in the midlatitudes of two hemispheres in the middle stratosphere, with significant ozone decrease in the NH and increase in ozone in the SH. Large biases exist in reanalyses, and it is still challenging to do trend analysis based on reanalysis ozone data. According to single-factor-controlled model simulations with WACCM, the temperature increase in the troposphere and the ozone decrease in the NH stratosphere are mainly connected to the increase in SST and subsequent changes of atmospheric circulations. Both the increase in SSTs and the decrease in ozone in the NH contribute to the temperature decrease in the NH stratosphere. The increase in temperature in the lower stratospheric tropics may be related to an increase in ozone in that region, while warming SSTs contribute to a cooling in that area.</p