Intersatellite Biases Research Articles

Evaluation of 10-Year NOAA/NASA Suomi NPP and NOAA-20 VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR) over Deep Convective Clouds

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi NPP (S-NPP) and the NOAA-20 satellites that provides state-of-the-art Earth observations for ocean, land, aerosol, and cloud applications. VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR, or Level 1b products) are calibrated and produced independently by The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) VIIRS science teams. Multiple versions of S-NPP and NOAA-20 VIIRS SDRs are available to date. This study evaluates the long-term calibration stability, biases, and inter-channel consistency of S-NPP and NOAA-20 VIIRS SDRs generated by NOAA and NASA over Deep Convective Clouds (DCC) to support downstream applications, especially climate data record studies. Five VIIRS RSB SDRs were analyzed in this study: (1) NOAA version 2 S-NPP VIIRS reprocessed SDRs (NOAA-NPP-V2, 2012–2020), (2) NASA Collection 1 S-NPP VIIRS SDRs (NASA-NPP-C1, 2012–2021), (3) NASA Collection 2 S-NPP VIIRS SDRs (NASA-NPP-C2, 2012–2021), (4) NOAA constant F-factor calibrated NOAA-20 VIIRS SDRs (NOAA-N20-ConstF, 2018–2021), and (5) NASA Collection 2 NOAA-20 VIIRS SDRs (NASA-N20-C2, 2018–2021). The DCC time series analysis results indicate that the calibrations of the three S-NPP VIIRS RSB SDRs are generally stable, with trends within ±0.1%/year for all RSBs, except for M3–M4 (all three S-NPP SDRs) and I3 (NASA-NPP-C1 only). The calibration of NASA-NPP-C2 SDRs is more uniform at individual detector levels. NOAA-NPP-V2 and NASA-NPP-C1 SDRs exhibit non-negligible time-dependent detector level degradation in M1–M4 (up to 1.5% in 2020–2021), causing striping in the SDR imagery. The biases between NOAA and NASA S-NPP VIIRS RSB SDRs are from 0.1% to 2.4%. The calibrations of the two NOAA-20 VIIRS RSB SDRs are also generally stable, with trends within ±0.16%/year. Small downward trends were observed in the visible and near-infrared (VIS/NIR) bands, and small upward trends were observed in the shortwave infrared (SWIR) bands for both NOAA and NASA NOAA-20 SDRs. The biases between NOAA and NASA NOAA-20 VIIRS RSB SDRs are nearly constant over time and within ±0.2% for VIS/NIR bands and ±0.7% for SWIR bands. There exists large inter-satellite biases between S-NPP and NOAA-20 VIIRS SDRs, especially in the VIS/NIR bands (up to 4.5% for NOAA SDRs and up to 7% for NASA SDRs). In addition, the DCC reflectance of S-NPP VIIRS RSB spectral bands is more consistent with that of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) than that of NOAA-20. Bands M4 and M9 seem out of family in all five S-NPP and NOAA-20 RSB SDRs evaluated.

Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions

Zhari Namco, a large lake in the Tibetan Plateau (TP), is sensitive to climate and environmental change. However, it is difficult to retrieve accurate and continuous lake levels for Zhari Namco. A robust strategy, including atmospheric delay correction, waveform retracking, outlier deletion, and inter-satellite adjustment, is proposed to generate a long-term series of lake levels for Zhari Namco through multi-altimeter data. Apparent biases are found in troposphere delay correction from different altimeter products and adjusted using an identical model. The threshold (20%) algorithm is employed for waveform retracking. The two-step method combining a sliding median filter and 2σ criterion is used to eliminate outliers. Tandem mission data of altimeters are used to estimate inter-satellite bias. Finally, a 27-year-long lake level time series of Zhari Namco are constructed using the TOPEX/Poseidon-Jason1/2/3 (T/P-Jason1/2/3) altimeter data from 1992 to 2019, resulting in an accuracy of 10.1 cm for T/P-Jason1/2/3. Temperature, precipitation, lake area, equivalent water height, and in situ gauge data are used for validation. The correlation coefficient more than 0.90 can be observed between this result and in situ gauge data. Compared with previous studies and existing database products, our method yields sequences with the best observational quality and the longest continuous monitoring in Zhari Namco. The time series indicates that the lake level in Zhari Namco has increased by ∼ 5.7 m, with an overall trend of 0.14 ± 0.01 m/yr, showing a fluctuating rate (1992–1999: −0.25 ± 0.05 m/yr, 2000–2008: 0.26 ± 0.04 m/yr, 2009–2016: −0.05 ± 0.03 m/yr, 2017–2019: 1.34 ± 0.34 m/yr). These findings will enhance the understanding of water budget and the effect of climate change in the TP.

A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.

A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures

Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

Improved GNSS-R Ocean Surface Altimetry With CYGNSS in the Seas of Indonesia

Ocean surface altimetry with GNSS reflectometry (GNSS-R) has the potential to improve the observation and retrieval of mesoscale ocean current flows by increasing the spatial density of ocean surface observations. The leading challenges in current GNSS-R altimetry investigations include precise delay retracking, correction of ionospheric effects, and spacecraft receiver positioning. Here, we present improved methods to account for these challenges. A reflection-model-based approach is presented for delay retracking that uses simulated delay-Doppler maps (DDMs) to retrieve the specular delay from measured DDMs. Global ionosphere maps are used to estimate the group delay effect along the direct and reflection paths. Precise orbit determination techniques are used to improve estimates of the receiving spacecraft position and reduce systematic intersatellite biases. We analyze altimetric height retrievals from two cases, strongly coherent or diffuse reflections. We study the residual systematic error signals with a focus on understanding the errors identified above. The strong coherent observations have narrow, symmetric delay waveforms and provide higher than average signal-to-noise ratio. The DDMs of diffuse observations, originate from relatively rough ocean surfaces and have the characteristic horse-shoe shape. Only measurements from the ocean and seas surrounding Indonesia are considered as this area provides a significant number of strongly coherent reflections. More than 50 000 single-point observations and week-long averaged results are presented to quantify the noise and systematic characteristics of the retrieved surface heights.

Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

Robust, Long-term Lake Level Change from Multiple Satellite Altimeters in Tibet: Observing the Rapid Rise of Ngangzi Co over a New Wetland

Satellite altimetry has been successfully applied to monitoring water level variation of global lakes. However, it is still difficult to retrieve accurate and continuous observations for most Tibetan lakes, due to their high altitude and rough terrain. Aiming to generate long-term and accurate lake level time series for the Tibetan lakes using multi-altimeters, we present a robust strategy including atmosphere delay corrections, waveform retracking, outlier removal and inter-satellite bias adjustment. Apparent biases in dry troposphere corrections from different altimeter products are found, and such correctios must be recalculated using the same surface pressure model. A parameter is defined to evaluate the performance of the retracking algorithm. The ICE retracker outperforms the 20% and 50% threshold retrackers in the case of Ngangzi Co, where a new wetland has been established. A two-step algorithm is proposed for outlier removal. Two methods are adopted to estimate inter-satellite bias for different cases of with and without overlap. Finally, a 25-year-long lake level time series of Ngangzi Co are constructed using the TOPEX/Poseidon-family altimeter data from October 1992 to December 2017, resulting in an accuracy of ~17 cm for TOPEX/Poseidon and ~10 cm for Jason-1/2/3. The accuracy of retrieved lake levels is on the order of decimeter. Because of no gauge data available, ICESat and SARAL data with the accuracy better than 7 cm are used for validation. A correlation more than 0.9 can be observed between the mean lake levels from TOPEX/Poseidon-family satellites, ICESat and SARAL. Compared to the previous studies and other available altimeter-derived lake level databases, our result is the most robust and has resulted in the maximum number of continuous samples. The time series indicates that the lake level of Ngangzi Co increased by ~8 m over 1998–2017 and changed with different rates in the past 25 years (-0.39 m/yr in 1992–1997, 1.03 m/yr in 1998–2002 and 0.32 m/yr in 2003–2014). These findings will enhance the understanding of water budget and the effect of climate change.

An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

AbstractState‐of‐the‐art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high‐frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along‐track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer‐term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state‐of‐the‐art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat‐2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03–0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03–0.06 m.

Characterisation of Special Sensor Microwave Water Vapor Profiler (SSM/T-2) radiances using radiative transfer simulations from global atmospheric reanalyses

The near-global and all-sky coverage of satellite observations from microwave humidity sounders operating in the 183GHz band complement radiosonde and aircraft observations and satellite infrared clear-sky observations. The Special Sensor Microwave Water Vapor Profiler (SSM/T-2) of the Defense Meteorological Satellite Program began operations late 1991. It has been followed by several other microwave humidity sounders, continuing today. However, expertise and accrued knowledge regarding the SSM/T-2 data record is limited because it has remained underused for climate applications and reanalyses. In this study, SSM/T-2 radiances are characterised using several global atmospheric reanalyses. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), the first ECMWF reanalysis of the 20th-century (ERA-20C), and the Japanese 55-year Reanalysis (JRA-55) are projected into SSM/T-2 radiance space using a fast radiative transfer model. The present study confirms earlier indications that the polarisation state of SSM/T-2 antenna is horizontal (not vertical) in the limit of nadir viewing. The study also formulates several recommendations to improve use of the SSM/T-2 measurement data in future fundamental climate data records or reanalyses. Recommendations are (1) to correct geolocation errors, especially for DMSP 14; (2) to blacklist poor quality data identified in the paper; (3) to correct for inter-satellite biases, estimated here on the order of 1K, by applying an inter-satellite recalibration or, for reanalysis, an automated (e.g., variational) bias correction; and (4) to improve precipitating cloud filtering or, for reanalysis, consider an all-sky assimilation scheme where radiative transfer simulations account for the scattering effect of hydrometeors.

The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: a long-term database for climate studies.

In this paper, we describe the construction of the Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database, which includes vertically resolved ozone and water vapor data from a subset of the limb profiling satellite instruments operating since the 1980s. The primary SWOOSH products are zonal-mean monthly-mean time series of water vapor and ozone mixing ratio on pressure levels (12 levels per decade from 316 to 1 hPa). The SWOOSH pressure level products are provided on several independent zonal-mean grids (2.5, 5, and 10°), and additional products include two coarse 3-D griddings (30° long × 10° lat, 20° × 5°) as well as a zonal-mean isentropic product. SWOOSH includes both individual satellite source data as well as a merged data product. A key aspect of the merged product is that the source records are homogenized to account for inter-satellite biases and to minimize artificial jumps in the record. We describe the SWOOSH homogenization process, which involves adjusting the satellite data records to a "reference" satellite using coincident observations during time periods of instrument overlap. The reference satellite is chosen based on the best agreement with independent balloon-based sounding measurements, with the goal of producing a long-term data record that is both homogeneous (i.e., with minimal artificial jumps in time) and accurate (i.e., unbiased). This paper details the choice of reference measurements, homogenization, and gridding process involved in the construction of the combined SWOOSH product and also presents the ancillary information stored in SWOOSH that can be used in future studies of water vapor and ozone variability. Furthermore, a discussion of uncertainties in the combined SWOOSH record is presented, and examples of the SWOOSH record are provided to illustrate its use for studies of ozone and water vapor variability on interannual to decadal timescales. The version 2.5 SWOOSH data are publicly available at doi:10.7289/V5TD9VBX.

Spatiotemporal densification of river water level time series by multimission satellite altimetry

AbstractLimitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data‐selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root‐Mean‐Square Error of 0.8 m, bias of −0.4 m, and Nash‐Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash‐Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube).

Recalibration and merging of SSU observations for stratospheric temperature trend studies

AbstractLong‐term observations from the Stratospheric Sounding Unit (SSU) during 1979–2006 onboard NOAA historical polar orbiting satellites were recalibrated for climate change investigation. A two‐point linear calibration equation, with cold space and an internal blackbody warm target as end‐point references, was used to transfer SSU raw counts data into radiances. The warm target temperature was represented by measurements from the space side thermistor on the blackbody, and the cold space radiance was assumed to be zero. Space view corrections due to an electrical interference were applied. Intersatellite calibration was conducted simultaneously by applying calibration offsets determined from residual intersatellite biases. The recalibration reached an accuracy of 0.1–0.2 K for global means and thus is expected to improve the consistency in stratospheric temperature time series in climate reanalyses. The recalibrated SSU radiances were further adjusted to develop Version 2 of the NOAA stratospheric temperature time series. The effects being adjusted included those from changes in instrument cell pressure and atmospheric carbon dioxide concentration, viewing angle differences, and semidiurnal tides due to orbital drift. Intersatellite biases were carefully removed to ensure smooth transitions between satellite pairs. Differences from Version 1 included improved radiance calibration, improved adjusting schemes for diurnal drift and intersatellite biases, removal of time‐varying cell pressure adjustment for NOAA‐9 channel 1, and excluding NOAA‐7 channel 2 in the time series. In addition to the final merged data set, intermediate synthetic time series corresponding to different adjustments were also created to quantify their impact on the final trend as well as its reliability and uncertainty. Excellent matching between satellite pairs, especially the 7 year overlaps between NOAA‐11 and NOAA‐14 during 1997–2004, in intermediate as well as the final time series provided strong evidence on the validity of adjustments and thus confidence on the resulting trends. The Version 2 global mean trends for 1979–2006 were −0.69 ± 0.18, −0.77 ± 0.15, and −0.85 ± 0.15 K/decade for SSU channels 1, 2, and 3, representing temperatures of middle stratosphere, upper stratosphere, and stratosphere‐mesosphere, respectively. Among these, cooling of channel 2 was stronger and channel 3 weaker than those in UK Met Office (UKMO) data by about 1 K during the entire SSU period from 1979 to 2006. Finally, the average of the channel 1 and channel 3 anomalies in Version 2 was close to channel 2 anomalies to within 0.2 K for the entire 1979–2006 period with identical trends. This feature was found consistent with chemistry‐climate model simulations.

Assessment of intercalibration methods for satellite microwave humidity sounders

Three methods for intercalibrating humidity sounding channels are compared to assess their merits and demerits. The methods use the following: (1) natural targets (Antarctica and tropical oceans), (2) zonal average brightness temperatures, and (3) simultaneous nadir overpasses (SNOs). Advanced Microwave Sounding Unit‐B instruments onboard the polar‐orbiting NOAA 15 and NOAA 16 satellites are used as examples. Antarctica is shown to be useful for identifying some of the instrument problems but less promising for intercalibrating humidity sounders due to the large diurnal variations there. Owing to smaller diurnal cycles over tropical oceans, these are found to be a good target for estimating intersatellite biases. Estimated biases are more resistant to diurnal differences when data from ascending and descending passes are combined. Biases estimated from zonal‐averaged brightness temperatures show large seasonal and latitude dependence which could have resulted from diurnal cycle aliasing and scene‐radiance dependence of the biases. This method may not be the best for channels with significant surface contributions. We have also tested the impact of clouds on the estimated biases and found that it is not significant, at least for tropical ocean estimates. Biases estimated from SNOs are the least influenced by diurnal cycle aliasing and cloud impacts. However, SNOs cover only relatively small part of the dynamic range of observed brightness temperatures.

Intersatellite bias of the high-resolution infrared radiation sounder water vapor channel determined using ISCCP B1 data

Information on the distribution and transport of upper tropospheric humidity helps our understanding of atmospheric circulation. However, efforts to develop a dataset that is not diurnally biased-that is, one that is based on geostationary data-have been hindered by a lack of intercalibration of the historical geostationary satellite water vapor channels. Toward this end, the high-resolution infrared radiation sounder (HIRS) intersatellite bias is determined by using collocated observations of geostationary and HIRS to compute monthly biases between HIRS instruments on different satellites. The resulting HIRS intersatellite bias then allows the construction of a temporally consistent HIRS record, from which geostationary satellite water vapor channels can be intercalibrated. This intercalibration will allow a uniform analysis of upper tropospheric water vapor.

Merging and Analysis of Elevation Time Series Over Greenland Ice Sheet From Satellite Radar Altimetry

Spatial–temporal variability and changes of Greenland ice sheet elevation from 1992 to 2008 are analyzed from merged ERS-1, ERS-2, and Envisat satellite radar altimeter data. A methodology for determining intersatellite biases was developed and applied in order to merge measurements from these different satellites and to create continuous and consistent time series. Intersatellite biases of elevation and backscatter coefficient have shown to be significantly affected by the bias between measurements in ascending and descending orbits. Adjustment of elevation time series for its dependence on backscatter coefficient and other waveform parameters performed in this paper substantially reduced the amplitude of elevation seasonal variations and locally corrected elevation change-rate estimates by up to several centimeters per year. It was found that the correction depends not only on the variations in the waveform parameters but also on the temporal variations of the correlation gradients, which represent the sensitivity of the elevation change to the change in the waveform parameters. An elevation change rate of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$+2.8 \pm 0.2\ \hbox{cm/year}$</tex></formula> from 1992 to 2008 over 76% of the Greenland ice sheet area was found. Increases in surface elevation from 1995 observed over the high-elevation regions of Greenland were followed by an elevation decrease from 2006. For the whole period of 1992–2008, the elevation increase is 4.0 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\pm$</tex></formula> 0.2 cm/year over 87% of the area above 1500 m. In contrast, over 38% of the low-elevation areas below 1500 m, the rate of elevation change is <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$-7.0 \pm 1.0\ \hbox{cm/year}$</tex></formula> , and the surface elevation decrease that started from 2000 has continued.

Intersatellite calibration of AMSU-A observations for weather and climate applications

[1] Long-term observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites NOAA 15, 16, 17, and 18 and European Meteorological Operational satellite program-A (MetOp-A) were intercalibrated using their overlap observations. Simultaneous nadir overpasses (SNOs) and global ocean mean differences between these satellites were used to characterize calibration errors and to obtain calibration coefficients. Calibration errors were found manifesting themselves as certain scatter or temporal patterns of intersatellite biases, such as well-defined seasonal cycles in the Arctic and Antarctic SNO difference time series or a unique pattern closely correlated to the instrument temperature variability induced by Solar Beta Angle (SBA) variations in global ocean mean difference time series. Analyses of these patterns revealed five different types of biases that need to be removed from existing prelaunch-calibrated AMSU-A observations, which include relatively stable intersatellite biases between most satellite pairs, bias drifts on NOAA 16 and channel 7 of MetOp-A, sun-heating-induced instrument temperature variability in radiances, scene temperature dependency in biases due to inaccurate calibration nonlinearity, and biases due to channel frequency shift from its prelaunch measurement in certain satellite channels. Level-1c time-dependent calibration offsets and nonlinear coefficients were introduced and determined from SNO and global ocean mean temperature regressions to remove or minimize the first four types of biases. Channel frequency shift in NOAA 15 channel 6 was obtained from the radiative transfer model simulation experiments. The new calibration coefficients and channel frequency values have significantly reduced the five different types of biases and resulted in more consistent multisatellite radiance observations for intercalibrated satellite channels. The intercalibrated AMSU-A observations have been merged with its precursor, the intercalibrated microwave sounding unit (MSU), to generate the NOAA/Center for Satellite Applications and Research (STAR) version 2.0 upper-air temperature climate data record (CDR) for climate trend and variability monitoring from 1979 to the present. The intercalibrated AMSU-A radiance data are expected to further improve accuracies of numerical weather prediction and consistencies in climate reanalysis and CDR developments.

Three decades of intersatellite-calibrated High-Resolution Infrared Radiation Sounder upper tropospheric water vapor

[1] To generate a climatologically homogenized time series of the upper tropospheric water vapor (UTWV), intersatellite calibration is carried out for 3 decades of High-Resolution Infrared Radiation Sounder (HIRS) channel 12 clear-sky measurements. Because of the independence of the individual satellite's instrument calibration, intersatellite biases exist from satellite to satellite. To minimize the expected intersatellite biases, measurement adjustments are derived from overlapping HIRS data from the equator to the poles to account for the large global temperature observation range. Examination of the intersatellite biases shows that the biases are scene temperature–dependent. Many overlapping satellites have bias variations of more than 0.5 K across the scene temperature ranges. An algorithm is developed to account for the varying biases with respect to brightness temperature. Analyses based on the intercalibrated data show that selected regions of UTWV are highly correlated with low-frequency indexes such as the Pacific Decadal Oscillation index and the Pacific and North America index, especially in the winter months. The derived upper tropospheric humidity in the central Pacific also corresponds well with the Nino 3.4 index. Thirty year trend analysis indicates an increase of upper tropospheric humidity in the equatorial tropics. The areal coverage of both high and low humidity values also increased over time. These features suggest the possibility of enhanced convective activity in the tropics.

Ionosphere monitoring and inter-frequency bias determination using Galileo: First results and future prospects

We present first results of using the European Global Navigation Satellite System (GNSS) Galileo for determining the Total Electron Content (TEC). Furthermore, we describe a calibration technique which can be used to determine GNSS inter-frequency and inter-system biases along with calibrated TEC. This technique is demonstrated by analysing two sets of data. The first data set covers Europe using GPS and GLONASS data provided by IGS ground stations, and GPS and GIOVE (Galileo In Orbit Validation Element) data collected by GIOVE sensor stations (GESS). The second data set uses GPS and GIOVE data from a global network of GESS. We find that the inter-frequency and inter-system biases are stable within the accuracy of the reconstruction and that the TEC values reconstructed from GIOVE data are compatible with TEC values obtained from GPS and GLONASS data. The RMS of the GIOVE-A/B inter-satellite biases, determined over a period of one month, is found to be below 1 TECU.

Multiple Scientific Uses of Radio Occultation: Global Navigation Satellite System Radio Occultation Workshop; Pasadena, California, 7–9 April 2009

There is a clear need for observing systems that characterize decadal‐scale changes of the climate system. A National Research Council (NRC) report, “Earth science and applications from space” (2007); recommends the radio occultation (RO) technique to monitor climate change. Radio occultation establishes a global climate benchmark by measuring the effect of atmospheric temperature, pressure, and water vapor on radio signals propagating above the outer edge of Earth's surface, known as the limb. The atmospherically induced delays are tied to atomic clock standards via the Global Positioning System (GPS). Measurements obtained decades apart can be intercompared without concern for intersatellite bias or calibration drift.