Partitioning Ground Motion Uncertainty When Conditioned on Station Data

ABSTRACT Rapid estimation of earthquake ground shaking and proper accounting of associated uncertainties in such estimates when conditioned on strong-motion station data or macroseismic intensity observations are crucial for downstream applications such as ground failure and loss estimation. The U.S. Geological Survey ShakeMap system is called upon to fulfill this objective in light of increased near-real-time access to strong-motion records from around the world. Although the station data provide a direct constraint on shaking estimates at specific locations, these data also heavily influence the uncertainty quantification at other locations. This investigation demonstrates methods to partition the within- (phi) and between-event (tau) uncertainty estimates under the observational constraints, especially when between-event uncertainties are heteroscedastic. The procedure allows the end users of ShakeMap to create separate between- and within-event realizations of ground-motion fields for downstream loss modeling applications in a manner that preserves the structure of the underlying random spatial processes.

Análisis de la precipitación y la evaporación en el Orinoco colombiano según los modelos climáticos regionales del experimento CORDEX-CORE

Con el fin de representar la precipitación y evaporación total mensual en una cuenca hidrográfica del Orinoco colombiano, este trabajo evaluó la capacidad de los modelos climáticos regionales incluidos en el Experimento regional coordinado de reducción de escala (CORDEX-CORE). Para ello, complementariamente, se incluyeron datos de precipitación y evaporación total de fuentes como Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), el reanálisis atmosférico (ERA5), Global Precipitation Climatology Center (GPCC) y Global Land Evaporation Amsterdam Model (GLEAM). Las comparaciones entre los ensambles de los modelos y las observaciones se hicieron utilizando métodos gráficos y métodos cuantitativos, entre ellos: diagramas de cajas, porcentajes de sesgo, eficiencia de Nash-Sutcliffe, entre otros. Los resultados evidencian que los valores promedio de precipitación están adecuadamente representados, en términos de su temporalidad y magnitud, por el ensamble del modelo RegCM, mientras que los valores promedio de evaporación total están mejor representados por el ensamble del modelo REMO en términos de la temporalidad, más no en su magnitud. Por otra parte, las estimaciones de caudal de largo plazo evidencian que los valores de evaporación total proporcionados por los modelos permiten una adecuada estimación del caudal promedio de largo plazo, pero no la adecuada estimación del ciclo anual de caudales. Este trabajo es pionero en la evaluación de los datos de precipitación y evaporación total mensual suministrados por CORDEX-CORE en el Orinoco colombiano, sienta precedentes para la incorporación de datos de modelos regionales para fines hidrológicos en zonas poco instrumentadas del país, y es el primer paso hacia la evaluación de escenarios regionalizados de cambio climático.

Network of Automatic Weather Stations : Pseudo random burst sequence type

A network of 125 Automatic Weather Stations (AWS) has been set up by India Meteorological Department (IMD) during the year 2006-07 across India. Each station is configured to measure air temperature, hourly maximum temperature, hourly minimum temperature, relative humidity, station level pressure, hourly rainfall and cumulative rainfall for the day, Wind speed and Wind direction. In addition to these parameters, 25 stations provide data for global solar radiation and soil temperature. Five stations also provide soil moisture in addition to soil temperature. Each station transmits a data stream at an interval of an hour in a Pseudo Random Burst Sequence (PRBS) manner via UHF transmitter and a dedicated meteorological satellite KALPANA-1/ INSAT-3A to the central AWS data receiving Earth Station facility established at IMD, Pune. Mean sea level pressure, dew point temperature, duration of bright sunshine and daily maximum & minimum temperature are derived at the receiving Earth Station. Data archival in near real time is done at the receiving Earth Station. Data dissemination in WMO code form is also done in near real time through Global Telecommunication System. This paper provides technical description of various sub-systems of PRBS type Indian Automatic Weather Station network including instrument, satellite transmission technique, sensor characteristics, siting and exposure conditions and performance of a representative station.

Does More Moisture in the Atmosphere Lead to More Intense Rains?

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatter plot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moistures in the atmosphere is that there is a more chance to happen, rather than of an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm/6h to a lesser extent. The result is derived from an analysis of 33 local rain–gauge station data and a shared sounding over Friuli Venezia Giulia, North–East Italy.

Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1

The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia and Brazil. The temporal correlation of simulated monthly precipitation sums against in-situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e., elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony–Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but allows to enforce a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and finally, also precipitation. Hence, this setup is the one providing the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered the biodiversity hotspot of Peru. The robustness of this particular parameterization option is backed up by similar results obtained during wet climate conditions observed in 2012.

Variabilité saisonnière et intra-saisonnière de la pluviométrie en milieu forestier dans le Sud-ouest centrafricain

Ce travail présente la variabilité intra-saisonnière de la pluviométrie dans le Sud-ouest centrafricain. Les données pluviométriques journalières utilisées couvrent la période 1981–2017. Elles ont été extraites aux points de grilles les plus proches des quatre stations représentatives de la zone d'étude (Bangui, Berberati, Boukoko et Nola) sur la base de données CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). Un contrôle de qualité de ces données a été effectué à partir d'une inter-comparaison entre les produits d'estimations et les données in-situ sur différentes sous-périodes : 1998–2011 à Nola, 1998–2012 à Berberati, 1998–2014 à Bangui et 2002–2017 à Boukoko. Le coefficient de corrélation entre les données CHIRPS et les observations au pas annuel est faible à Bangui (r=0,46), moyen à Nola (r=0,57) et Berberati (r=0,60), et bon à Boukoko (r=0,73). Les dates de début de la saison de pluies varient entre le 13 février et le 2 avril et celles de fin entre le 31 octobre et le 4 décembre. Des épisodes secs et humides sont mis en évidence dans le Sud-ouest centrafricain. A Bangui et Berberati, la période du 15 au 22 mai 1999 a été sèche soit 23 jours sans pluie. A Boukoko et Nola, la période du 26 juin au 24 juillet de la même année était caractérisée par des épisodes secs. Ces résultats établissent que la répartition intra-saisonnière des pluies n'est pas uniforme dans le Sud-ouest centrafricain.

Evaluation of hydrological modeling using climatic station and gridded precipitation dataset

The conventional rainfall data estimates are relatively accurate at some points of the region. The interpolation of such type of data approximates the actual rainfield however in data scarce regions; the resulted rainfield is the rough estimate of the actual rainfall events. In data scarce regions like Indus basin Pakistan, the data obtained through remote sensing can be very useful. This research evaluates two types of gridded data i.e., European Reanalysis (ERA) interim and Japanese Reanalysis 55 years (JRA-55) along with the climatic station data for three small dams in Pakistan. Since no measured flow data is available at these dams, the nearest possible catchments where flow data is available are calibrated and the calibrated parameters of these catchments are then used in actual dams for simulating the flow from all the three types of data using Soil and Water Assessment Tool (SWAT). The results of the comparison of gridded and rainguage precipitation shows that gridded data highly overestimates the climatic station data. Similar results were observed in the comparison of flow simulated by SWAT model. The Peak flood calculated from JRA-55 overestimates while the Era-Interim peak floods are comparable to that of climatic stations in two of the three catchments.

Challenges in Observation-Based Mapping of Daily Precipitation across the Conterminous United States

There is a great need for gridded daily precipitation datasets to support a wide variety of disciplines in science and industry. Production of such datasets faces many challenges, from station data ingest to gridded dataset distribution. The quality of the dataset is directly related to its information content, and each step in the production process provides an opportunity to maximize that content. The first opportunity is maximizing station density from a variety of sources, and assuring high quality through intensive screening, including manual review. To accommodate varying data latency times, the PRISM Climate Group releases eight versions of a day’s precipitation grid, from 24 hours after day’s end to six months elapsed time. The second opportunity is to distribute the station data to a grid using methods that add information and minimize the smoothing effect of interpolation. We use two competing methods, one that utilizes the information in long-term precipitation climatologies, and the other using weather radar return patterns. Finally, maintaining consistency among different time scales (monthly vs. daily) affords the opportunity to exploit information available at each scale. Maintaining temporal consistency over longer time scales is at cross purposes with maximizing information content. We therefore produce two datasets, one that maximizes data sources, and a second that includes only networks with long-term stations and no radar (a short-term data source). Further work is underway to improve station metadata, refine interpolation methods by producing climatologies targeted to specific storm conditions, and employ higher-resolution radar products.