Effects of Sample Rates and Data Latency on Digital Autopilots. Volume I. Analysis Summary.

1981 ◽  
Author(s):  
A. J. Ventre ◽  
J. F. Meadows ◽  
W. F. Anderson ◽  
J. R. Kennamer ◽  
J. W. Kesting
Keyword(s):  
Data Latency

scholarly journals So, How Much of the Earth’s Surface Is Covered by Rain Gauges?

2017 ◽  
Vol 98 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Chris Kidd ◽  
Andreas Becker ◽  
George J. Huffman ◽  
Catherine L. Muller ◽  
Paul Joe ◽  
...  
Keyword(s):  
Data Access ◽  
Rain Gauges ◽  
Temporal Sampling ◽  
Global Precipitation Climatology Centre ◽  
Integration Period ◽  
Wide Range ◽  
Equivalent Area ◽  
Tennis Court ◽  
Global Precipitation ◽  
Data Latency

Abstract The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earth’s surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near–real time to about 100,000 for all “official” gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)–available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earth’s surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

NAS surveillance data latency requirements: History and rationale

2014 ◽  
Author(s):  
Michael McVeigh ◽  
Marissa Gant ◽  
Clifton Baldwin ◽  
Naoum Anagnos ◽  
E. Michael O'Neill ◽  
...  
Keyword(s):  
Surveillance Data ◽  
Data Latency

scholarly journals Commercial Microwave Links as a tool for operational rainfall monitoring in Northern Italy

2020 ◽  
Author(s):  
Giacomo Roversi ◽  
Pier Paolo Alberoni ◽  
Anna Fornasiero ◽  
Federico Porcù
Keyword(s):  
Northern Italy ◽  
False Alarms ◽  
Number Of False Alarms ◽  
Basin Scale ◽  
Single Link ◽  
Precipitation Estimates ◽  
Data Source ◽  
The Impact ◽  
Data Latency ◽  
Commercial Microwave Links

Abstract. There is a growing interest in emerging opportunistic sensors for precipitation estimates, motivated by the need to describe with detail precipitation structures. In this work a preliminary assessment of the accuracy of Commercial Microwave Links (CMLs) retrieved rainfall rates in northern Italy is presented. The CML product, obtained by the publicly available RAINLINK package, is evaluated at different scales (single link, 5 km x 5 km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the Regional Weather Service of Emilia-Romagna, in northern Italy. The results of the 15 min single-link validation with close-by raingauges show high variability, with influence of the area physiography and precipitation patterns and the impact of some known issues (e.g. melting layer). However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show performances (R2 of 0.47 and CV of 0.77) which are very similar, when not even better, to satellite- and adjusted radar-based precipitation gridded products. This is especially true when basin-scale total precipitation amounts are considered (R2 of 0.85 and CV of 0.63). Taking into account also delays in the availability of the data (latency of 0.33 hours for CML against 1 hour for the adjusted radar and 24 h for the quality controlled raingauges), CMLs appear as a valuable data source in particular from a local operational framework perspective. A diffuse underestimation is evident at both grid box (Mean Error of −0.26) and basin scale (Multiplicative Bias of 0.7), while the number of false alarms is generally low and gets even lower as coverage increases. Finally, results show complementary strengths for CMLs and radars, encouraging a joint exploitation.

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

2021 ◽  
Author(s):  
Christopher Daly ◽  
Matthew K. Doggett ◽  
Joseph I. Smith ◽  
Keith V. Olson ◽  
Michael D. Halbleib ◽  
...  
Keyword(s):  
Information Content ◽  
Time Scales ◽  
Daily Precipitation ◽  
Temporal Consistency ◽  
Station Data ◽  
Data Source ◽  
Data Latency ◽  
Term Data

AbstractThere 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.

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