ALBERT

Description: Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

Description: An operational weather diagnostics application for automatic generation of wind fields in near–real time from observations delivered by the high-density WegenerNet meteorological station network in the Feldbach region of Austria is introduced. The purpose of the application is to empirically provide near-surface wind fields of very high spatial and temporal resolution for evaluating convection-permitting climate models as well as investigating weather and climate variability on a local scale. The diagnostic California Meteorological Model (CALMET) is used as the core tool. This model computes 3D wind fields based on observational weather data, a digital elevation model, and land-use categories. The application first produces the required input files from the WegenerNet stations and subsequently runs the CALMET model based on this input. In a third step the modeled wind fields are stored in the WegenerNet data archives every 30 min with a spatial resolution of 100 m × 100 m, while also generating averaged weather and climate products during postprocessing. The performance of the modeling against station observations, for which wind speeds were classified into weak and strong wind speeds, is evaluated and reasonably good results were found for both wind speed classes. The statistical agreement for the vector-mean wind speed is slightly better for weak wind speeds than for strong ones while the difference between modeled and observed wind directions is smaller for strong wind speeds than for weak ones. The application is also a valuable tool for other high-density networks.

Description: Extreme convective precipitation on subhourly scales is notoriously misrepresented in rain gauge-based observations, but uncertainties are weakly quantified at the 1 to 30 km scale. We employ a unique observing network, the high-density WegenerNet and surrounding operational rain gauge network in southeastern Austria, to sample convective precipitation extremes at these scales. By systematically constructing lower-density networks, we explore how estimated maximum area precipitation depends on observing station density. Using subhourly to hourly temporal resolution, we find a d−0.5(±0.1) power law decay of the event maximum area precipitation over distances d from 1 to 30 km, showing that operational gauge networks underrate extreme convective precipitation falling over small areas. Furthermore, extremes at point scale are found underestimated by operational networks by about 20%. We consider the dependencies representative for short-duration convective events over similar regions at midlatitudes and the results valuable for high-resolution climate model evaluation. ©2018. The Authors.

Description: The Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) is one of the new-generation payloads onboard the Chinese FengYun 3 (FY-3) series of operational meteorological satellites for sounding the Earth's neutral atmosphere and ionosphere. The GNOS was designed for acquiring setting and rising radio occultation (RO) data by using GNSS signals from both the Chinese BeiDou System (BDS) and the US Global Positioning System (GPS). An ultra-stable oscillator with 1 s stability (Allan deviation) at the level of 10−12 was installed on the FY-3C GNOS, and thus both zero-difference and single-difference excess phase processing methods should be feasible for FY-3C GNOS observations. In this study we focus on evaluating zero-difference processing of BDS RO data vs. single-difference processing, in order to investigate the zero-difference feasibility for this new instrument, which after its launch in September 2013 started to use BDS signals from five geostationary orbit (GEO) satellites, five inclined geosynchronous orbit (IGSO) satellites and four medium Earth orbit (MEO) satellites. We used a 3-month set of GNOS BDS RO data (October to December 2013) for the evaluation and compared atmospheric bending angle and refractivity profiles, derived from single- and zero-difference excess phase data, against co-located profiles from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. We also compared against co-located refractivity profiles from radiosondes. The statistical evaluation against these reference data shows that the results from single- and zero-difference processing are reasonably consistent in both bias and standard deviation, clearly demonstrating the feasibility of zero differencing for GNOS BDS RO observations. The average bias (and standard deviation) of the bending angle and refractivity profiles were found to be about 0.05 to 0.2 % (and 0.7 to 1.6 %) over the upper troposphere and lower stratosphere. Zero differencing was found to perform slightly better, as may be expected from its lower vulnerability to noise. The validation results indicate that GNOS can provide, on top of GPS RO profiles, accurate and precise BDS RO profiles both from single- and zero-difference processing. The GNOS observations by the series of FY-3 satellites are thus expected to provide important contributions to numerical weather prediction and global climate change analysis.

Description: The Global Navigation Satellite System (GNSS) radio occultation (RO) technique is widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source to RO at upper stratospheric altitudes, and a linear dual-frequency bending angle correction is commonly used to remove the first-order ionospheric effect. However, the higher-order residual ionospheric error (RIE) can still be significant, so it needs to be further mitigated for high-accuracy applications, especially from 35 km altitude upward, where the RIE is most relevant compared to the decreasing magnitude of the atmospheric bending angle. In a previous study we quantified RIEs using an ensemble of about 700 quasi-realistic end-to-end simulated RO events, finding typical RIEs at the 0.1 to 0.5 µrad noise level, but were left with 26 exceptional events with anomalous RIEs at the 1 to 10 µrad level that remained unexplained. In this study, we focused on investigating the causes of the high RIE of these exceptional events, employing detailed along-ray-path analyses of atmospheric and ionospheric refractivities, impact parameter changes, and bending angles and RIEs under asymmetric and symmetric ionospheric structures. We found that the main causes of the high RIEs are a combination of physics-based effects – where asymmetric ionospheric conditions play the primary role, more than the ionization level driven by solar activity – and technical ray tracer effects due to occasions of imperfect smoothness in ionospheric refractivity model derivatives. We also found that along-ray impact parameter variations of more than 10 to 20 m are possible due to ionospheric asymmetries and, depending on prevailing horizontal refractivity gradients, are positive or negative relative to the initial impact parameter at the GNSS transmitter. Furthermore, mesospheric RIEs are found generally higher than upper-stratospheric ones, likely due to being closer in tangent point heights to the ionospheric E layer peaking near 105 km, which increases RIE vulnerability. In the future we will further improve the along-ray modeling system to fully isolate technical from physics-based effects and to use it beyond this work for additional GNSS RO signal propagation studies.

Description: The demand for high-quality atmospheric data records, which are applicable in climate studies, is undisputed. Using such records requires knowledge of the quality and the specific characteristics of all contained data sources. The latest version of the Wegener Center (WEGC) multi-satellite Global Positioning System (GPS) radio occultation (RO) record, OPSv5.6, provides globally distributed upper-air satellite data of high quality, usable for climate and other high-accuracy applications. The GPS RO technique has been deployed in several satellite missions since 2001. Consistency among data from these missions is essential to create a homogeneous long-term multi-satellite climate record. In order to enable a qualified usage of the WEGC OPSv5.6 data set we performed a detailed analysis of satellite-dependent quality aspects from 2001 to 2017. We present the impact of the OPSv5.6 quality control on the processed data and reveal time-dependent and satellite-specific quality characteristics. The highest quality data are found for MetOp (Meteorological Operational satellite) and GRACE (Gravity Recovery and Climate Experiment). Data from FORMOSAT-3/COSMIC (Formosa Satellite mission-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) are also of high quality. However, comparatively large day-to-day variations and satellite-dependent irregularities need to be taken into account when using these data. We validate the consistency among the various satellite missions by calculating monthly mean temperature deviations from the multi-satellite mean, including a correction for the different sampling characteristics. The results are highly consistent in the altitude range from 8 to 25 km, with mean temperature deviations less than 0.1 K. At higher altitudes the OPSv5.6 RO temperature record is increasingly influenced by the characteristics of the bending angle initialization, with the amount of impact depending on the receiver quality.

Description: A weather diagnostic application for automatic generation of gridded wind fields in near-real-time, recently developed by the authors Schlager et al. (2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains 11 meteorological stations at elevations from about 600 to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 min from the WegenerNet JBT, mean wind and wind gust fields at 10 and 50 m height levels with a high spatial resolution of 100 m × 100 m and a temporal resolution of 30 min. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. The main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance. Due to the spatially more homogeneous wind speeds and directions for strong wind events in this mountainous region, the results show somewhat better performance for these events. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from 5-year climate data of the WegenerNet JBT in comparison to 9-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al. (2017). In general, the 5-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with, and evaluation of, wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

Description: A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.