Evaluation of GPM-DPR precipitation estimates with WegenerNet gauge data

The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore, independent validations using direct precipitation observation on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of the measurements depend on various influencing factors. In this way, a validation may help to minimise those uncertainties. The DPR provides three different radar rain rate estimates for the GPM core satellite: Ku-band-only rain rates, Ka-band-only rain rates and a product combining the two frequencies. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local scale terrestrial network of 153 meteorological stations in southeast Austria. The validation is based on a graphical and a statistical approach using only data where both Ku- and Ka-band measurements are available. The data delivered from the WegenerNet are gauge-based gridded rainfall observations; the meteorological winter is excluded due to technical reasons. The focus lies on the resemblance of the variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. During the last four years 22 rainfall events were observed by the GPM-DPR over the WegenerNet and the analysis rests upon these rainfall events. The WegenerNet provides a large number of gauges within each GPM-DPR footprint. Its biases are well studied and corrected, thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver the best results due to the high number of light rainfall events

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

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 < 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

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

An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2010. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend within about (−0.2±0.1) K/10 yr (95% confidence interval) at all latitudes. This signals a systematic deviation of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the southern hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; the trend in the anomaly differences is generally very small. Regarding (A)MSU, its poor vertical resolution poses another problem by missing important features of the vertical atmospheric structure. This points to the advantage of homogeneously distributed measurements with high vertical resolution

Quantifying uncertainty in climatological fields from GPS radio occultation: an empirical-analytical error model

Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10° zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40°. Beyond 40°, this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be only about 30% of the original one or less. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07% in refractivity and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at altitudes below about 6 km (high latitudes) to 10 km (low latitudes)

GNSS remote sensing of the Australian tropopause

Radio occultation (RO) techniques that use signals transmitted by Global Navigation Satellite Systems (GNSS) have emerged over the past decade as an important tool for measuring global changes in tropopause temperature and height, a valuable capacity given the tropopause’s sensitivity to temperature variations. This study uses 45,091 RO data from the CHAMP (CHAllenging Minisatellite Payload, 80 months), GRACE (Gravity Recovery And Climate Experiment, 23 months) and COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate, 20 months) satellites to analyse the variability of the tropopause’s height and temperature over Australia. GNSS RO temperature profiles from CHAMP, GRACE, and COSMIC are first validated using radiosonde observations provided by the Bureau of Meteorology (Australia). These are compared to RO soundings from between 2001 and 2007 that occurred within 3 h and 100 km of a radiosonde.The results indicate that RO soundings provide data of a comparable quality to radiosonde observations in the tropopause region, with temperature deviations of less than 0.5 ± 1.5 K. An analysis of tropopause height and temperature anomalies indicates a height increase over Australia as a whole of ca. 4.8 ± 1.3 m between September 2001 and April 2008, with a corresponding temperature decrease of −0.019 ± 0.007 K. A similar pattern of increasing height/decreasing temperature was generally observed when determining the spatial distribution of the tropopause height and temperature rate of change over Australia. Although only a short period has been considered in this study, a function of the operating time of these satellites, the results nonetheless show an increase in the height of the tropopause over Australia during this period and thus may indicate regional warming. Several mechanisms could be responsible for these changes, such as an increase in the concentration of greenhouse gases in the atmosphere, and lower stratospheric cooling due to ozone loss, both of which have been observed during the last decades

Quantification of structural uncertainty in climate data records from GPS radio occultation

Global Positioning System (GPS) radio occultation (RO) has provided continuous observations of the Earth's atmosphere since 2001 with global coverage, all-weather capability, and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS). Precise time measurements enable long-term stability but careful processing is needed. Here we provide climate-oriented atmospheric scientists with multicenter-based results on the long-term stability of RO climatological fields for trend studies. We quantify the structural uncertainty of atmospheric trends estimated from the RO record, which arises from current processing schemes of six international RO processing centers, DMI Copenhagen, EUM Darmstadt, GFZ Potsdam, JPL Pasadena, UCAR Boulder, and WEGC Graz. Monthly-mean zonal-mean fields of bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature from the CHAMP mission are compared for September 2001 to September 2008. We find that structural uncertainty is lowest in the tropics and mid-latitudes (50° S to 50° N) from 8 km to 25 km for all inspected RO variables. In this region, the structural uncertainty in trends over 7 yr is <0.03% for bending angle, refractivity, and pressure, <3 m for geopotential height of pressure levels, and <0.06 K for temperature; low enough for detecting a climate change signal within about a decade. Larger structural uncertainty above about 25 km and at high latitudes is attributable to differences in the processing schemes, which undergo continuous improvements. Though current use of RO for reliable climate trend assessment is bound to 50° S to 50° N, our results show that quality, consistency, and reproducibility are favorable in the UTLS for the establishment of a climate benchmark record

Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchmentscale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km x 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10% of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤ 20% normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44% for the 99.9th and by 25% for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01°. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study