Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate

Global Precipitation Measurement: Evolution of Algorithms from TRMM to GPM

Topics include level 1C processing of Global Precipitation Measurement (GPM) Microwave Images (GMI) and intercalibration, radar-enhanced radiometer GPROF radiometer retrieval algorithm (radiometer-RE), combined radar-radiometer algorithms, and merged algorithm product

Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors

GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm

The Global Precipitation Measurement (GPM) mission measures global precipitation at a temporal resolution of a few hours to enable close monitoring of the global hydrological cycle. GPM achieves this by combining observations from a spaceborne precipitation radar, a constellation of passive microwave (PMW) sensors, and geostationary satellites. The Goddard Profiling Algorithm (GPROF) is used operationally to retrieve precipitation from all PMW sensors of the GPM constellation. Since the resulting precipitation rates serve as input for many of the level 3 retrieval products, GPROF constitutes an essential component of the GPM processing pipeline. This study investigates ways to improve GPROF using modern machine learning methods. We present two neuralnetwork-based, probabilistic implementations of GPROF: GPROF-NN 1D, which (just like the current GPROF implementation) processes pixels individually, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. The accuracy of the retrievals is evaluated using a test dataset consistent with the data used in the development of the GPROF and GPROF-NN retrievals. This allows for assessing the accuracy of the retrieval method isolated from the representativeness of the training data, which remains a major source of uncertainty in the development of precipitation retrievals. Despite using the same input information as GPROF, the GPROF-NN 1D retrieval improves the accuracy of the retrieved surface precipitation for the GPM Microwave Imager (GMI) from 0.079 to 0.059mmh 1 in terms of mean abso- lute error (MAE), from 76.1% to 69.5% in terms of symmetric mean absolute percentage error (SMAPE) and from 0 :797 to 0 :847 in terms of correlation. The improvements for the Microwave Humidity Sounder (MHS) are from 0.085 to 0.061mmh 1 in terms of MAE, from 81% to 70.1% for SMAPE, and from 0 :724 to 0 :804 in terms of correlation. Comparable improvements are found for the retrieved hydrometeor profiles and their column integrals, as well as the detection of precipitation. Moreover, the ability of the retrievals to resolve small-scale variability is improved by more than 40% for GMI and 29% for MHS. The GPROFNN 3D retrieval further improves the MAE to 0.043mmh 1; the SMAPE to 48.67 %; and the correlation to 0:897 for GMI and 0.043mmh 1, 63.42 %, and 0:83 for MHS. Application of the retrievals to GMI observations of Hurricane Harvey shows moderate improvements when compared to co-located GPM-combined and ground-based radar measurements indicating that the improvements at least partially carry over to assessment against independent measurements. Similar retrievals for MHS do not show equally clear improvements, leaving the validation against independent measurements for future investigation. Both GPROF-NN algorithms make use of the same input and output data as the original GPROF algorithm and thus may replace the current implementation in a future update of the GPM processing pipeline. Despite their superior accuracy, the single-core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms promise to be a simple and cost-efficient way to increase the accuracy of the PMW precipitation retrievals of the GPM constellation and thus improve the monitoring of the global hydrological cycle

Forming a Mogi Doughnut in the Years Prior to and Immediately Before the 2014 M8.1 Iquique, Northern Chile, Earthquake

Asperities are patches where the fault surfaces stick until they break in earthquakes. Locating asperities and understanding their causes in subduction zones is challenging because they are generally located offshore. We use seismicity, interseismic and coseismic slip, and the residual gravity field to map the asperity responsible for the 2014M8.1 Iquique, Chile, earthquake. For several years prior to the mainshock, seismicity occurred exclusively downdip of the asperity. Two weeks before the mainshock, a series of foreshocks first broke the upper plate then the updip rim of the asperity. This seismicity formed a ring around the slip patch (asperity) that later ruptured in the mainshock. The asperity correlated both with high interseismic locking and a circular gravity low, suggesting that it is controlled by geologic structure. Most features of the spatiotemporal seismicity pattern can be explained by a mechanical model in which a single asperity is stressed by relative plate motion

Daily Precipitation over Southern Africa: A New Resource for Climate Studies

This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed

A new seismicity catalogue of the eastern Alps using the temporary Swath-D network

We present a new, consistently processed seismicity catalogue for the eastern and southern Alps based on the temporary dense Swath-D monitoring network. The final catalogue contains 6053 earthquakes for the time period 2017–2019 and has a magnitude of completeness of −1.0 ML. The smallest detected and located events have a magnitude of −1.7 ML. Aimed at the low to moderate seismicity in the study region, we have developed a multi-stage, mostly automatic workflow that combines a priori information from local catalogues and waveform-based event detection, subsequent efficient GPU-based (GPU: graphics processing unit) event search by template matching, P and S arrival time pick refinement, and location in a regional 3-D velocity model. The resulting seismicity distribution generally confirms the previously identified main seismically active domains but provides increased resolution of the fault activity at depth. In particular, the high number of small events additionally detected by the template search contributes to a denser catalogue and provides an important basis for future geological and tectonic studies in this complex part of the Alpine orogen.</p

The Presampler for the Forward and Rear Calorimeter in the ZEUS Detector

The ZEUS detector at HERA has been supplemented with a presampler detector in front of the forward and rear calorimeters. It consists of a segmented scintillator array read out with wavelength-shifting fibers. We discuss its desi gn, construction and performance. Test beam data obtained with a prototype presampler and the ZEUS prototype calorimeter demonstrate the main function of this detector, i.e. the correction for the energy lost by an electron interacting in inactive material in front of the calorimeter.Comment: 20 pages including 16 figure

The role of tropical-extratropical interaction and synoptic variability in maintaining the South Pacific Convergence Zone in CMIP5 models

The South Pacific Convergence Zone (SPCZ) is simulated as too zonal a feature in current generation climate models, including those in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). This zonal bias induces errors in tropical convective heating, with subsequent effects on global circulation. The SPCZ structure, particularly in the subtropics, is governed by the tropical-extratropical interaction between transient synoptic systems and the mean background state. However, the fidelity of synoptic-scale interactions as simulated by CMIP5 models has not yet been evaluated. In this study, analysis of synoptic variability in the simulated subtropical SPCZ reveals that the basic mechanism of tropical-extratropical interaction is generally well simulated, with storms approaching the SPCZ along comparable trajectories to observations. However, there is a broad spread in mean precipitation and its variability across the CMIP5 ensemble. Inter-model spread appears to relate to a biased background state in which the synoptic waves propagate. In particular, the region of mean negative zonal stretching deformation or "storm graveyard" in the upper troposphere?a feature previously determined to play a key role in SPCZ-storm interactions?is typically displaced in CMIP5 models to the northeast of its position in reanalysis data, albeit with individual model graveyards displaying a pronounced (25 degree) longitudinal spread. From these findings, we suggest that SPCZs simulated by CMIP5 models are not simply too zonal; rather, in models the subtropical SPCZ manifests a diagonal tilt similar to observations while SST biases force an overly zonal tropical SPCZ, resulting in a more disjointed SPCZ than observed

The contribution of rain gauges in the calibration of the IMERG product: results from the first validation over Spain

After 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough qualitycontrolled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ~ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.Funding from projects CGL2013- 48367-P, CGL2016-78702-C2-1-R, CGL2016-80609-R (Ministerio de Economía y Competitividad), UNCM08-1E-086 (Ministerio de Ciencia e Innovación), and Development of Numerical Weather Prediction and Data Application Technique 1365002970/KMA2018-00721 (Korea Meteorological Administration) is gratefully acknowledged