Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

NASA Astrophysics Data System (ADS)

Panu, U. S.; Ng, W.; Rasmussen, P. F.

2009-12-01

The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in

Computation of rainfall erosivity from daily precipitation amounts.

PubMed

Beguería, Santiago; Serrano-Notivoli, Roberto; Tomas-Burguera, Miquel

2018-10-01

Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatial distribution of the daily precipitation concentration index in Southern Russia

NASA Astrophysics Data System (ADS)

Vyshkvarkova, Elena; Voskresenskaya, Elena; Martin-Vide, Javier

2018-05-01

The territory of Southern Russia presents a great diversity of climates and complex orography that lead to a very different precipitation distribution. Annual precipitation amounts differ between 222 mm in the coast of the Caspian Sea and > 2000 mm in the highest parts of the Caucasus Mountains. In order to investigate the statistical structure of daily precipitation across the study region the daily precipitation Concentration Index (CI) was used. In present paper, the CI was calculated for 42 meteorological stations during the 1970-2010 period. The analysis of precipitation concentration identified that the distribution of daily precipitation is more regular over the west, north and south regions compared to the east (the Caspian Sea coast and the Caspian Depression). The Crimean peninsula is characterized by low CI values in the north and high values in the eastern part.

Benchmarking a geostatistical procedure for the homogenisation of annual precipitation series

NASA Astrophysics Data System (ADS)

Caineta, Júlio; Ribeiro, Sara; Henriques, Roberto; Soares, Amílcar; Costa, Ana Cristina

2014-05-01

The European project COST Action ES0601, Advances in homogenisation methods of climate series: an integrated approach (HOME), has brought to attention the importance of establishing reliable homogenisation methods for climate data. In order to achieve that, a benchmark data set, containing monthly and daily temperature and precipitation data, was created to be used as a comparison basis for the effectiveness of those methods. Several contributions were submitted and evaluated by a number of performance metrics, validating the results against realistic inhomogeneous data. HOME also led to the development of new homogenisation software packages, which included feedback and lessons learned during the project. Preliminary studies have suggested a geostatistical stochastic approach, which uses Direct Sequential Simulation (DSS), as a promising methodology for the homogenisation of precipitation data series. Based on the spatial and temporal correlation between the neighbouring stations, DSS calculates local probability density functions at a candidate station to detect inhomogeneities. The purpose of the current study is to test and compare this geostatistical approach with the methods previously presented in the HOME project, using surrogate precipitation series from the HOME benchmark data set. The benchmark data set contains monthly precipitation surrogate series, from which annual precipitation data series were derived. These annual precipitation series were subject to exploratory analysis and to a thorough variography study. The geostatistical approach was then applied to the data set, based on different scenarios for the spatial continuity. Implementing this procedure also promoted the development of a computer program that aims to assist on the homogenisation of climate data, while minimising user interaction. Finally, in order to compare the effectiveness of this methodology with the homogenisation methods submitted during the HOME project, the obtained results

Extending the Precipitation Map Offshore Using Daily and 3-Hourly Combined Precipitation Estimates

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Curtis, Scott; Einaudi, Franco (Technical Monitor)

2001-01-01

One of the difficulties in studying landfalling extratropical cyclones along the Pacific Coast is the lack of antecedent data over the ocean, including precipitation. Recent research on combining various satellite-based precipitation estimates opens the possibility of realistic precipitation estimates on a global 1 deg. x 1 deg. latitude-longitude grid at the daily or even 3-hourly interval. The goal in this work is to provide quantitative precipitation estimates that correctly represent the precipitation- related variables in the hydrological cycle: surface accumulations (fresh-water flux into oceans), frequency and duration statistics, net latent heating, etc.

The sensitivity of WRF daily summertime simulations over West Africa to alternative parameterizations. Part 2: Precipitation.

PubMed

Noble, Erik; Druyan, Leonard M; Fulakeza, Matthew

2016-01-01

This paper evaluates the performance of the Weather and Research Forecasting (WRF) model as a regional-atmospheric model over West Africa. It tests WRF sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during eleven consecutive years, 2000-2010. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface-hydrology, and PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African Easterly Waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time-longitude correlations (against GPCP) of between 0.35-0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model-v.3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell-Devenyi convection scheme, resulting in higher correlations against observations than using the Kain-Fritch convection scheme. Other parameterizations have less obvious impact. Validation statistics for optimized WRF configurations simulating the parallel period during 2000-2010 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation.

The sensitivity of WRF daily summertime simulations over West Africa to alternative parameterizations. Part 2: Precipitation

PubMed Central

Noble, Erik; Druyan, Leonard M.; Fulakeza, Matthew

2018-01-01

This paper evaluates the performance of the Weather and Research Forecasting (WRF) model as a regional-atmospheric model over West Africa. It tests WRF sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during eleven consecutive years, 2000–2010. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface-hydrology, and PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African Easterly Waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time-longitude correlations (against GPCP) of between 0.35–0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model-v.3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell-Devenyi convection scheme, resulting in higher correlations against observations than using the Kain-Fritch convection scheme. Other parameterizations have less obvious impact. Validation statistics for optimized WRF configurations simulating the parallel period during 2000–2010 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation. PMID:29563651

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

HOMPRA Europe - A gridded precipitation data set from European homogenized time series

NASA Astrophysics Data System (ADS)

Rustemeier, Elke; Kapala, Alice; Meyer-Christoffer, Anja; Finger, Peter; Schneider, Udo; Venema, Victor; Ziese, Markus; Simmer, Clemens; Becker, Andreas

2017-04-01

(Becker et al., 2013 and Schamm et al., 2014). Caussinus, H., und O. Mestre, 2004: Detection and correction of artificial shifts in climate series, Journal of the Royal, Statistical Society. Series C (Applied Statistics), 53(3), 405-425. Mestre, O., 2003: Correcting climate series using ANOVA technique, Proceedings of the fourth seminar Willmott, C.; Rowe, C. & Philpot, W., 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring The American Carthographer, 12, 5-16 Becker, A.; Finger, P.; Meyer-Christoffer, A.; Rudolf, B.; Schamm, K.; Schneider, U. & Ziese, M., 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present Earth System Science Data, 5, 71-99 Schamm, K.; Ziese, M.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Schneider, U.; Schröder, M. & Stender, P., 2014: Global gridded precipitation over land: a description of the new GPCC First Guess Daily product, Earth System Science Data, 6, 49-60

Effective precipitation duration for runoff peaks based on catchment modelling

NASA Astrophysics Data System (ADS)

Sikorska, A. E.; Viviroli, D.; Seibert, J.

2018-01-01

Despite precipitation intensities may greatly vary during one flood event, detailed information about these intensities may not be required to accurately simulate floods with a hydrological model which rather reacts to cumulative precipitation sums. This raises two questions: to which extent is it important to preserve sub-daily precipitation intensities and how long does it effectively rain from the hydrological point of view? Both questions might seem straightforward to answer with a direct analysis of past precipitation events but require some arbitrary choices regarding the length of a precipitation event. To avoid these arbitrary decisions, here we present an alternative approach to characterize the effective length of precipitation event which is based on runoff simulations with respect to large floods. More precisely, we quantify the fraction of a day over which the daily precipitation has to be distributed to faithfully reproduce the large annual and seasonal floods which were generated by the hourly precipitation rate time series. New precipitation time series were generated by first aggregating the hourly observed data into daily totals and then evenly distributing them over sub-daily periods (n hours). These simulated time series were used as input to a hydrological bucket-type model and the resulting runoff flood peaks were compared to those obtained when using the original precipitation time series. We define then the effective daily precipitation duration as the number of hours n, for which the largest peaks are simulated best. For nine mesoscale Swiss catchments this effective daily precipitation duration was about half a day, which indicates that detailed information on precipitation intensities is not necessarily required to accurately estimate peaks of the largest annual and seasonal floods. These findings support the use of simple disaggregation approaches to make usage of past daily precipitation observations or daily precipitation simulations

Changes to the temporal distribution of daily precipitation

NASA Astrophysics Data System (ADS)

Rajah, Kailash; O'Leary, Tess; Turner, Alice; Petrakis, Gabriella; Leonard, Michael; Westra, Seth

2014-12-01

Changes to the temporal distribution of daily precipitation were investigated using a data set of 12,513 land-based stations from the Global Historical Climatology Network. The distribution of precipitation was measured using the Gini index (which describes how uniformly precipitation is distributed throughout a year) and the annual number of wet days. The Mann-Kendall test and a regression analysis were used to assess the direction and rate of change to both indices. Over the period of 1976-2000, East Asia, Central America, and Brazil exhibited a decrease in the number of both wet and light precipitation days, and eastern Europe exhibited a decrease in the number of both wet and moderate precipitation days. In contrast, the U.S., southern South America, western Europe, and Australia exhibited an increase in the number of both wet and light precipitation days. Trends in both directions were field significant at the global scale.

Large uncertainties in observed daily precipitation extremes over land

NASA Astrophysics Data System (ADS)

Herold, Nicholas; Behrangi, Ali; Alexander, Lisa V.

2017-01-01

We explore uncertainties in observed daily precipitation extremes over the terrestrial tropics and subtropics (50°S-50°N) based on five commonly used products: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset, the Global Precipitation Climatology Centre-Full Data Daily (GPCC-FDD) dataset, the Tropical Rainfall Measuring Mission (TRMM) multi-satellite research product (T3B42 v7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Global Precipitation Climatology Project's One-Degree Daily (GPCP-1DD) dataset. We use the precipitation indices R10mm and Rx1day, developed by the Expert Team on Climate Change Detection and Indices, to explore the behavior of "moderate" and "extreme" extremes, respectively. In order to assess the sensitivity of extreme precipitation to different grid sizes we perform our calculations on four common spatial resolutions (0.25° × 0.25°, 1° × 1°, 2.5° × 2.5°, and 3.75° × 2.5°). The impact of the chosen "order of operation" in calculating these indices is also determined. Our results show that moderate extremes are relatively insensitive to product and resolution choice, while extreme extremes can be very sensitive. For example, at 0.25° × 0.25° quasi-global mean Rx1day values vary from 37 mm in PERSIANN-CDR to 62 mm in T3B42. We find that the interproduct spread becomes prominent at resolutions of 1° × 1° and finer, thus establishing a minimum effective resolution at which observational products agree. Without improvements in interproduct spread, these exceedingly large observational uncertainties at high spatial resolution may limit the usefulness of model evaluations. As has been found previously, resolution sensitivity can be largely eliminated by applying an order of operation where indices are calculated prior to regridding. However, this approach is not appropriate when true area averages are desired

Extreme daily precipitation in the Northern Sierra Precipitation 8-Station index: The combined impact of landfalling atmospheric rivers and the Sierra barrier jet

NASA Astrophysics Data System (ADS)

Cordeira, J. M.; Ralph, F. M.; Neiman, P. J.; Hughes, M.

2014-12-01

The Upper Sacramento River area is vital to California's water supply, and is susceptible to major floods. Recent studies indicate that orographic precipitation in this complex terrain involves both inland penetrating atmospheric rivers (ARs) and the Sierra barrier jet (SBJ). The southerly SBJ induces orographic precipitation along south-facing slopes in the Shasta region, whereas landfalling ARs ascend up and over the statically stable SBJ and induce orographic precipitation along west-facing upper slopes in the Northern Sierra Nevada. This paper explores the hypothesis that extreme daily precipitation here is controlled by the presence of both a landfalling AR and a SBJ. Three 10-year-long (2000-2011) observational datasets are used. ARs are identified from the Neiman et al. (2008) AR catalog that uses an SSM/I satellite-based AR-detection method from Ralph et al. (2004), whereas SBJ conditions are determined from Chico, CA wind profiler data using the method from Neiman et al. (2010). Extreme daily precipitation is identified from the average of 8 rain gauges spanning the region known as the "Northern Sierra 8-Station Index." The "index" is used by water managers to assess water supply. Extreme events are defined as the 50 largest daily precipitation totals in the index for the 10-year period (the top ~1.37%). These dates in the 8-station index are compared with the catalogs of landfalling ARs and SBJs. In summary, 46 of 50 (92%) extreme daily precipitation events are associated with landfalling ARs on either the day before or the day of precipitation, whereas 45 of 50 (90%) extreme daily precipitation events are associated with SBJ conditions. 38 of 50 (76%) extreme daily precipitation events are associated with both a landfalling AR and an SBJ. The 10 days with the largest daily precipitation in the index were all associated with both a landfalling AR and an SBJ. Thus, extreme daily precipitation in Northern California is strongly controlled by the presence of

[Stochastic characteristics of daily precipitation and its spatiotemporal difference over China based on information entropy].

PubMed

Li, Xin Xin; Sang, Yan Fang; Xie, Ping; Liu, Chang Ming

2018-04-01

Daily precipitation process in China showed obvious randomness and spatiotemporal variation. It is important to accurately understand the influence of precipitation changes on control of flood and waterlogging disaster. Using the daily precipitation data measured at 520 stations in China during 1961-2013, we quantified the stochastic characteristics of daily precipitation over China based on the index of information entropy. Results showed that the randomness of daily precipitation in the southeast region were larger than that in the northwest region. Moreover, the spatial distribution of stochastic characteristics of precipitation was different at various grades. Stochastic characteri-stics of P 0 (precipitation at 0.1-10 mm) was large, but the spatial variation was not obvious. The stochastic characteristics of P 10 (precipitation at 10-25 mm) and P 25 (precipitation at 25-50 mm) were the largest and their spatial difference was obvious. P 50 (precipitation ≥50 mm) had the smallest stochastic characteristics and the most obviously spatial difference. Generally, the entropy values of precipitation obviously increased over the last five decades, indicating more significantly stochastic characteristics of precipitation (especially the obvious increase of heavy precipitation events) in most region over China under the scenarios of global climate change. Given that the spatial distribution and long-term trend of entropy values of daily precipitation could reflect thespatial distribution of stochastic characteristics of precipitation, our results could provide scientific basis for the control of flood and waterlogging disaster, the layout of agricultural planning, and the planning of ecological environment.

Daily Temperature and Precipitation Data for 223 Former-USSR Stations (NDP-040)

DOE Data Explorer

Razuvaev, V. N. [Russian Research Institute of Hydrometeorological Information-World Data Centre; Apasova, E. B. [Russian Research Institute of Hydrometeorological Information-World Data Centre; Martuganov, R. A. [Russian Research Institute of Hydrometeorological Information-World Data Centre

1990-01-01

The stations in this dataset are considered by RIHMI to comprise one of the best networks suitable for temperature and precipitation monitoring over the the former-USSR. Factors involved in choosing these 223 stations included length or record, amount of missing data, and achieving reasonably good geographic coverage. There are indeed many more stations with daily data over this part of the world, and hundreds more station records are available through NOAA's Global Historical Climatology Network - Daily (GHCND) database. The 223 stations comprising this database are included in GHCND, but different data processing, updating, and quality assurance methods/checks mean that the agreement between records will vary depending on the station. The relative quality and accuracy of the common station records in the two databases also cannot be easily assessed. As of this writing, most of the common stations contained in the GHCND have more recent records, but not necessarily records starting as early as the records available here. This database contains four variables: daily mean, minimum, and maximum temperature, and daily total precipitation (liquid equivalent). Temperature were taken three times a day from 1881-1935, four times a day from 1936-65, and eight times a day since 1966. Daily mean temperature is defined as the average of all observations for each calendar day. Daily maximum/minimum temperatures are derived from maximum/minimum thermometer measurements. See the measurement description file for further details. Daily precipitation totals are also available (to the nearest tenth of a millimeter) for each station. Throughout the record, daily precipitation is defined as the total amount of precipitation recorded during a 24-h period, snowfall being converted to a liquid total by melting the snow in the gauge. From 1936 on, rain gauges were checked several times each day; the cumulative total of all observations during a calendar day was presumably used as the

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Flood triggering in Switzerland: the role of daily to monthly preceding precipitation

NASA Astrophysics Data System (ADS)

Froidevaux, P.; Schwanbeck, J.; Weingartner, R.; Chevalier, C.; Martius, O.

2015-09-01

Determining the role of different precipitation periods for peak discharge generation is crucial for both projecting future changes in flood probability and for short- and medium-range flood forecasting. In this study, catchment-averaged daily precipitation time series are analyzed prior to annual peak discharge events (floods) in Switzerland. The high number of floods considered - more than 4000 events from 101 catchments have been analyzed - allows to derive significant information about the role of antecedent precipitation for peak discharge generation. Based on the analysis of precipitation times series, a new separation of flood-related precipitation periods is proposed: (i) the period 0 to 1 day before flood days, when the maximum flood-triggering precipitation rates are generally observed, (ii) the period 2 to 3 days before flood days, when longer-lasting synoptic situations generate "significantly higher than normal" precipitation amounts, and (iii) the period from 4 days to 1 month before flood days when previous wet episodes may have already preconditioned the catchment. The novelty of this study lies in the separation of antecedent precipitation into the precursor antecedent precipitation (4 days before floods or earlier, called PRE-AP) and the short range precipitation (0 to 3 days before floods, a period when precipitation is often driven by one persistent weather situation like e.g., a stationary low-pressure system). A precise separation of "antecedent" and "peak-triggering" precipitation is not attempted. Instead, the strict definition of antecedent precipitation periods permits a direct comparison of all catchments. The precipitation accumulating 0 to 3 days before an event is the most relevant for floods in Switzerland. PRE-AP precipitation has only a weak and region-specific influence on flood probability. Floods were significantly more frequent after wet PRE-AP periods only in the Jura Mountains, in the western and eastern Swiss plateau, and at

Spatial downscaling and correction of precipitation and temperature time series to high resolution hydrological response units in the Canadian Rocky Mountains

NASA Astrophysics Data System (ADS)

Kienzle, Stefan

2015-04-01

Precipitation is the central driving force of most hydrological processes, and is also the most variable element of the hydrological cycle. As the precipitation to runoff ratio is non-linear, errors in precipitation estimations are amplified in streamflow simulations. Therefore, the accurate estimate of areal precipitation is essential for watershed models and relevant impacts studies. A procedure is presented to demonstrate the spatial distribution of daily precipitation and temperature estimates across the Rocky Mountains within the framework of the ACRU agro-hydrological modelling system (ACRU). ACRU (Schulze, 1995) is a physical-conceptual, semi-distributed hydrological modelling system designed to be responsive to changes in land use and climate. The model has been updated to include specific high-mountain and cold climate routines and is applied to simulate impacts of land cover and climate change on the hydrological behaviour of numerous Rocky Mountain watersheds in Alberta, Canada. Both air temperature and precipitation time series need to be downscaled to hydrological response units (HRUs), as they are the spatial modelling units for the model. The estimation of accurate daily air temperatures is critical for the separation of rain and snow. The precipitation estimation procedure integrates a spatially distributed daily precipitation database for the period 1950 to 2010 at a scale of 10 by 10 km with a 1971-2000 climate normal database available at 2 by 2 km (PRISM). Resulting daily precipitation time series are further downscaled to the spatial resolution of hydrological response units, defined by 100 m elevation bands, land cover, and solar radiation, which have an average size of about 15 km2. As snow measurements are known to have a potential under-catch of up to 40%, further adjustment of snowfall may need to be increased using a procedure by Richter (1995). Finally, precipitation input to HRUs with slopes steeper than 10% need to be further corrected

Trend analysis for daily rainfall series of Barcelona

NASA Astrophysics Data System (ADS)

Ortego, M. I.; Gibergans-Báguena, J.; Tolosana-Delgado, R.; Egozcue, J. J.; Llasat, M. C.

2009-09-01

Frequency analysis of hydrological series is a key point to acquire an in-depth understanding of the behaviour of hydrologic events. The occurrence of extreme hydrologic events in an area may imply great social and economical impacts. A good understanding of hazardous events improves the planning of human activities. A useful model for hazard assessment of extreme hydrologic events in an area is the point-over-threshold (POT) model. Time-occurrence of events is assumed to be Poisson distributed, and the magnitude X of each event is modeled as an arbitrary random variable, whose excesses over the threshold x0, Y = X - x0, given X > x0, have a Generalized Pareto Distribution (GPD), ( ? )- 1? FY (y|β,?) = 1 - 1+ βy , 0 ? y < ysup , where ysup = +? if ? 0, and ysup = -β? ? if ? < 0. The limiting distribution for ? = 0 is an exponential one. Independence between this magnitude and occurrence in time is assumed, as well as independence from event to event. In order to take account for uncertainty of the estimation of the GPD parameters, a Bayesian approach is chosen. This approach allows to include necessary conditions on the parameters of the distribution for our particular phenomena, as well as propagate adequately the uncertainty of estimations to the hazard parameters, such as return periods. A common concern is to know whether magnitudes of hazardous events have changed in the last decades. Long data series are very appreciated in order to properly study these issues. The series of daily rainfall in Barcelona (1854-2006) has been selected. This is one of the longer european daily rainfall series available. Daily rainfall is better described using a relative scale and therefore it is suitably treated in a log-scale. Accordingly, log-precipitation is identified with X. Excesses over a threshold are modeled by a GPD with a limited maximum value. An additional assumption is that the distribution of the excesses Y has limited upper tail and, therefore, ? < 0, ysup

Atlas of interoccurrence intervals for selected thresholds of daily precipitation in Texas

USGS Publications Warehouse

Asquith, William H.; Roussel, Meghan C.

2003-01-01

A Poisson process model is used to define the distribution of interoccurrence intervals of daily precipitation in Texas. A precipitation interoccurrence interval is the time period between two successive rainfall events. Rainfall events are defined as daily precipitation equaling or exceeding a specified depth threshold. Ten precipitation thresholds are considered: 0.05, 0.10, 0.25, 0.50, 0.75, 1.0, 1.5, 2.0, 2.5, and 3.0 inches. Site-specific mean interoccurrence interval and ancillary statistics are presented for each threshold and for each of 1,306 National Weather Service daily precipitation gages. Maps depicting the spatial variation across Texas of the mean interoccurrence interval for each threshold are presented. The percent change from the statewide standard deviation of the interoccurrence intervals to the root-mean-square error ranges from a magnitude minimum of (negative) -24 to a magnitude maximum of -60 percent for the 0.05- and 2.0-inch thresholds, respectively. Because of the substantial negative percent change, the maps are considered more reliable estimators of the mean interoccurrence interval for most locations in Texas than the statewide mean values.

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

Operational quality control of daily precipitation using spatio-climatological consistency testing

NASA Astrophysics Data System (ADS)

Scherrer, S. C.; Croci-Maspoli, M.; van Geijtenbeek, D.; Naguel, C.; Appenzeller, C.

2010-09-01

Quality control (QC) of meteorological data is of utmost importance for climate related decisions. The search for an effective automated QC of precipitation data has proven difficult and many weather services still use mainly manual inspection of daily precipitation including MeteoSwiss. However, man power limitations force many weather services to move towards less labour intensive and more automated QC with the challenge to keeping data quality high. In the last decade, several approaches have been presented to objectify daily precipitation QC. Here we present a spatio-climatological approach that will be implemented operationally at MeteoSwiss. It combines the information from the event based spatial distribution of everyday's precipitation field and the historical information of the interpolation error using different precipitation intensity intervals. Expert judgement shows that the system is able to detect potential outliers very well (hardly any missed errors) without creating too many false alarms that need human inspection. 50-80% of all flagged values have been classified as real errors by the data editor. This is much better than the roughly 15-20% using standard spatial regression tests. Very helpful in the QC process is the automatic redistribution of accumulated several day sums. Manual inspection in operations can be reduced and the QC of precipitation objectified substantially.

A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

EPA Science Inventory

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

Changes and Attribution of Extreme Precipitation in Climate Models: Subdaily and Daily Scales

NASA Astrophysics Data System (ADS)

Zhang, W.; Villarini, G.; Scoccimarro, E.; Vecchi, G. A.

2017-12-01

Extreme precipitation events are responsible for numerous hazards, including flooding, soil erosion, and landslides. Because of their significant socio-economic impacts, the attribution and projection of these events is of crucial importance to improve our response, mitigation and adaptation strategies. Here we present results from our ongoing work.In terms of attribution, we use idealized experiments [pre-industrial control experiment (PI) and 1% per year increase (1%CO2) in atmospheric CO2] from ten general circulation models produced under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the fraction of attributable risk to examine the CO2 effects on extreme precipitation at the sub-daily and daily scales. We find that the increased CO2 concentration substantially increases the odds of the occurrence of sub-daily precipitation extremes compared to the daily scale in most areas of the world, with the exception of some regions in the sub-tropics, likely in relation to the subsidence of the Hadley Cell. These results point to the large role that atmospheric CO2 plays in extreme precipitation under an idealized framework. Furthermore, we investigate the changes in extreme precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5°C and 2°C temperature targets. We find that the frequency of annual extreme precipitation at a global scale increases in both 1.5°C and 2°C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative. Overall, the frequency of global annual extreme precipitation is similar between 1.5°C and 2°C for the period 2006-2035, and the changes in extreme precipitation in individual seasons are consistent with those for the entire year. The frequency of extreme precipitation in the 2°C experiments is higher than for the 1.5°C experiment after the late 2030s, particularly for the period 2071-2100.

Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin, Iran

NASA Astrophysics Data System (ADS)

Taie Semiromi, M.; Koch, M.

2017-12-01

Although linear/regression statistical downscaling methods are very straightforward and widely used, and they can be applied to a single predictor-predictand pair or spatial fields of predictors-predictands, the greatest constraint is the requirement of a normal distribution of the predictor and the predictand values, which means that it cannot be used to predict the distribution of daily rainfall because it is typically non-normal. To tacked with such a limitation, the current study aims to introduce a new developed hybrid technique taking advantages from Artificial Neural Networks (ANNs), Wavelet and Quantile Mapping (QM) for downscaling of daily precipitation for 10 rain-gauge stations located in Gharehsoo River Basin, Iran. With the purpose of daily precipitation downscaling, the study makes use of Second Generation Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma). Climate projections are available for three representative concentration pathways (RCPs) namely RCP 2.6, RCP 4.5 and RCP 8.5 for up to 2100. In this regard, 26 National Centers for Environmental Prediction (NCEP) reanalysis large-scale variables which have potential physical relationships with precipitation, were selected as candidate predictors. Afterwards, predictor screening was conducted using correlation, partial correlation and explained variance between predictors and predictand (precipitation). Depending on each rain-gauge station between two and three predictors were selected which their decomposed details (D) and approximation (A) obtained from discrete wavelet analysis were fed as inputs to the neural networks. After downscaling of daily precipitation, bias correction was conducted using quantile mapping. Out of the complete time series available, i.e. 1978-2005, two third of which namely 1978-1996 was used for calibration of QM and the reminder, i.e. 1997-2005 was considered for the validation. Result showed that the proposed

Spatial analysis of precipitation time series over the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Latif, Yasir; Yaoming, Ma; Yaseen, Muhammad

2018-01-01

The upper Indus basin (UIB) holds one of the most substantial river systems in the world, contributing roughly half of the available surface water in Pakistan. This water provides necessary support for agriculture, domestic consumption, and hydropower generation; all critical for a stable economy in Pakistan. This study has identified trends, analyzed variability, and assessed changes in both annual and seasonal precipitation during four time series, identified herein as: (first) 1961-2013, (second) 1971-2013, (third) 1981-2013, and (fourth) 1991-2013, over the UIB. This study investigated spatial characteristics of the precipitation time series over 15 weather stations and provides strong evidence of annual precipitation by determining significant trends at 6 stations (Astore, Chilas, Dir, Drosh, Gupis, and Kakul) out of the 15 studied stations, revealing a significant negative trend during the fourth time series. Our study also showed significantly increased precipitation at Bunji, Chitral, and Skardu, whereas such trends at the rest of the stations appear insignificant. Moreover, our study found that seasonal precipitation decreased at some locations (at a high level of significance), as well as periods of scarce precipitation during all four seasons. The observed decreases in precipitation appear stronger and more significant in autumn; having 10 stations exhibiting decreasing precipitation during the fourth time series, with respect to time and space. Furthermore, the observed decreases in precipitation appear robust and more significant for regions at high elevation (>1300 m). This analysis concludes that decreasing precipitation dominated the UIB, both temporally and spatially including in the higher areas.

Oxygen and hydrogen stable isotope content in daily-collected precipitation samples at Dome C, East Antarctica

NASA Astrophysics Data System (ADS)

Dreossi, Giuliano; Stenni, Barbara; Del Guasta, Massimo; Bonazza, Mattia; Grigioni, Paolo; Karlicek, Daniele; Mognato, Riccardo; Scarchilli, Claudio; Turchetti, Filippo; Zannoni, Daniele

2016-04-01

Antarctic ice cores allow to obtain exceptional past climate records, thanks to their water stable isotope content, which provides integrated tracers of the atmospheric water cycle and local climate. Low accumulation sites of the East Antarctic plateau provide the oldest ice core records, with the record-breaking EPICA Dome C drilling covering the last eight climate cycles. However, the isotope-temperature relationship, commonly used to derive the temperature, may be characterized by significant geographical and temporal variations. Moreover, post-depositional effects may further complicate the climate interpretation. A continuous series of precipitation data is needed in order to gain a better understanding of the factors affecting the water stable isotopes in Antarctic precipitation at a specific site. In this study, we use the first and so-far only multi-year series of daily precipitation sampling and isotope measurements from the French-Italian Concordia Station, located at Dome C in East Antarctica (75°06'S 123°21'E; elevation: 3233 m a.s.l.; mean annual temperature: -54.5°C; snow accumulation rate: 25 kg m-2 yr-1), where the oldest deep Antarctic ice core has been retrieved. Surface air temperature data have been provided by the US automatic weather station (AWS), placed 1.5 km away from the base, while tropospheric temperature profiles are obtained by means of a radiosonde, launched once per day by the IPEV/Italian Antarctic Meteo-climatological Observatory. The new dataset also enables us for the first time to study the isotope-temperature relationship distinguishing between different types of precipitation, namely diamond dust, hoar frost and snowfall, identified by the observations carried out by the winter-over personnel collecting the snow samples. Here we present the complete data series of water stable isotopes in precipitation at Dome C spanning the time period from 2008 to 2014, in the framework of the PNRA PRE-REC project.

Homogenization of long instrumental temperature and precipitation series over the Spanish Northern Coast

NASA Astrophysics Data System (ADS)

Sigro, J.; Brunet, M.; Aguilar, E.; Stoll, H.; Jimenez, M.

2009-04-01

will be widely discussed. Initial comparisons with rapidly growing speleothems in two different caves indicate that speleothem trace element ratios like Ba/Ca are recording the decrease in littoral precipitation in the last several decades. References Aguilar, E., Auer, I., Brunet, M., Peterson, T. C. and Weringa, J. 2003. Guidelines on Climate Metadata and Homogenization, World Meteorological Organization (WMO)-TD no. 1186 / World Climate Data and Monitoring Program (WCDMP) no. 53, Geneva: 51 pp. Brunet M, Saladié O, Jones P, Sigró J, Aguilar E, Moberg A, Lister D, Walther A, Almarza C. 2008. A case-study/guidance on the development of long-term daily adjusted temperature datasets, WMO-TD-1425/WCDMP-66, Geneva: 43 pp. Jones, P D, and Hulme M, 1996, Calculating regional climatic time series for temperature and precipitation: Methods and illustrations, Int. J. Climatol., 16, 361- 377. Osborn, T. J., Briffa K. R., and Jones P. D., 1997, Adjusting variance for sample-size in tree-ring chronologies and other regional mean time series, Dendrochronologia, 15, 89- 99.

Climatology of extreme daily precipitation in Colorado and its diverse spatial and seasonal variability

USGS Publications Warehouse

Mahoney, Kelly M.; Ralph, F. Martin; Walter, Klaus; Doesken, Nolan; Dettinger, Michael; Gottas, Daniel; Coleman, Timothy; White, Allen

2015-01-01

The climatology of Colorado’s historical extreme precipitation events shows a remarkable degree of seasonal and regional variability. Analysis of the largest historical daily precipitation totals at COOP stations across Colorado by season indicates that the largest recorded daily precipitation totals have ranged from less than 60 mm day−1 in some areas to more than 250 mm day−1 in others. East of the Continental Divide, winter events are rarely among the top 10 events at a given site, but spring events dominate in and near the foothills; summer events are most common across the lower-elevation eastern plains, while fall events are most typical for the lower elevations west of the Divide. The seasonal signal in Colorado’s central mountains is complex; high-elevation intense precipitation events have occurred in all months of the year, including summer, when precipitation is more likely to be liquid (as opposed to snow), which poses more of an instantaneous flood risk. Notably, the historic Colorado Front Range daily rainfall totals that contributed to the damaging floods in September 2013 occurred outside of that region’s typical season for most extreme precipitation (spring–summer). That event and many others highlight the fact that extreme precipitation in Colorado has occurred historically during all seasons and at all elevations, emphasizing a year-round statewide risk.

DAPAGLOCO - A global daily precipitation dataset from satellite and rain-gauge measurements

NASA Astrophysics Data System (ADS)

Spangehl, T.; Danielczok, A.; Dietzsch, F.; Andersson, A.; Schroeder, M.; Fennig, K.; Ziese, M.; Becker, A.

2017-12-01

The BMBF funded project framework MiKlip(Mittelfristige Klimaprognosen) develops a global climate forecast system on decadal time scales for operational applications. Herein, the DAPAGLOCO project (Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized) provides a global precipitation dataset as a combination of microwave-based satellite measurements over ocean and rain gauge measurements over land on daily scale. The DAPAGLOCO dataset is created for the evaluation of the MiKlip forecast system in the first place. The HOAPS dataset (Hamburg Ocean Atmosphere Parameter and Fluxes from Satellite data) is used for the derivation of precipitation rates over ocean and is extended by the use of measurements from TMI, GMI, and AMSR-E, in addition to measurements from SSM/I and SSMIS. A 1D-Var retrieval scheme is developed to retrieve rain rates from microwave imager data, which also allows for the determination of uncertainty estimates. Over land, the GPCC (Global Precipitation Climatology Center) Full Data Daily product is used. It consists of rain gauge measurements that are interpolated on a regular grid by ordinary Kriging. The currently available dataset is based on a neuronal network approach, consists of 21 years of data from 1988 to 2008 and is currently extended until 2015 using the 1D-Var scheme and with improved sampling. Three different spatial resolved dataset versions are available with 1° and 2.5° global, and 0.5° for Europe. The evaluation of the MiKlip forecast system by DAPAGLOCO is based on ETCCDI (Expert Team on Climate Change and Detection Indices). Hindcasts are used for the index-based comparison between model and observations. These indices allow for the evaluation of precipitation extremes, their spatial and temporal distribution as well as for the duration of dry and wet spells, average precipitation amounts and percentiles on global scale. Besides, an ETCCDI-based climatology of the DAPAGLOCO

A hybrid orographic plus statistical model for downscaling daily precipitation in Northern California

USGS Publications Warehouse

Pandey, G.R.; Cayan, D.R.; Dettinger, M.D.; Georgakakos, K.P.

2000-01-01

A hybrid (physical-statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5?? ?? 2.5?? gridded National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January-March over the period of 1988-95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

Evaluation and intercomparison of GPM-IMERG and TRMM 3B42 daily precipitation products over Greece

NASA Astrophysics Data System (ADS)

Kazamias, A. P.; Sapountzis, M.; Lagouvardos, K.

2017-09-01

Accurate precipitation data at high temporal and spatial resolutions are needed for numerous applications in hydrology, water resources management and flood risk management. Satellite-based precipitation estimations/products offer a potential alternative source of rainfall data for regions with sparse rain gauge network. The recently launched Global Precipitation Measurement (GPM) mission is the successor of Tropical Rainfall Measuring Mission (TRMM) providing global precipitation estimates at spatial resolution of 0.1 degree x 0.1 degree and half-hourly temporal resolution. This study aims at evaluating the accuracy of the Integrated Multi-satellite Retrievals for GPM (IMERG) near-real-time daily product (GPM-3IMERGDL) against rain gauge observations from a network of stations distributed across Greece for the year 2016. Moreover, the GPM-IMERG product is also compared with its predecessor, the Version-7 near-real-time (3B42RT) daily product of TRMM Multisatellite Precipitation Analysis (TMPA). Several statistical metrics are used to quantitatively evaluate the performance of the satellite-based precipitation estimates against rain gauge observations. In addition, categorical statistical indices are used to assess rain detection capabilities of the two satellite products. The GPM-IMERG daily product shows reasonable agreement (CC=0.60) against rain gauge observations, with the exception of coastal areas in which low correlations are achieved. The GPM-IMERG daily precipitation product tends to overestimate rainfall, especially in complex terrain areas with high annual precipitation. In particular, rainfall estimates in western Greece have a strong positive bias. On the other hand, the TRMM 3B42 product shows low correlation (CC=0.45) against rain gauge observations and slightly underestimates rainfall. This study is a first attempt to evaluate and compare the newly introduced GPM-IMERG and the TRMM 3B42 rainfall products at daily timescale over Greece.

When will trends in European mean and heavy daily precipitation emerge?

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-03-01

A multi-model ensemble of regional climate projections for Europe is employed to investigate how the time of emergence (TOE) for seasonal sums and maxima of daily precipitation depends on spatial scale. The TOE is redefined for emergence from internal variability only; the spread of the TOE due to imperfect climate model formulation is used as a measure of uncertainty in the TOE itself. Thereby, the TOE becomes a fundamentally limiting timescale and translates into a minimum spatial scale on which robust conclusions can be drawn about precipitation trends. Thus, minimum temporal and spatial scales for adaptation planning are also given. In northern Europe, positive winter trends in mean and heavy precipitation, and in southwestern and southeastern Europe, summer trends in mean precipitation already emerge within the next few decades. However, across wide areas, especially for heavy summer precipitation, the local trend emerges only late in the 21st century or later. For precipitation averaged to larger scales, the trend, in general, emerges earlier.

Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations

NASA Astrophysics Data System (ADS)

Beranová, Romana; Kyselý, Jan; Hanel, Martin

2018-04-01

The study compares characteristics of observed sub-daily precipitation extremes in the Czech Republic with those simulated by Hadley Centre Regional Model version 3 (HadRM3) and Rossby Centre Regional Atmospheric Model version 4 (RCA4) regional climate models (RCMs) driven by reanalyses and examines diurnal cycles of hourly precipitation and their dependence on intensity and surface temperature. The observed warm-season (May-September) maxima of short-duration (1, 2 and 3 h) amounts show one diurnal peak in the afternoon, which is simulated reasonably well by RCA4, although the peak occurs too early in the model. HadRM3 provides an unrealistic diurnal cycle with a nighttime peak and an afternoon minimum coinciding with the observed maximum for all three ensemble members, which suggests that convection is not captured realistically. Distorted relationships of the diurnal cycles of hourly precipitation to daily maximum temperature in HadRM3 further evidence that underlying physical mechanisms are misrepresented in this RCM. Goodness-of-fit tests indicate that generalised extreme value distribution is an applicable model for both observed and RCM-simulated precipitation maxima. However, the RCMs are not able to capture the range of the shape parameter estimates of distributions of short-duration precipitation maxima realistically, leading to either too many (nearly all for HadRM3) or too few (RCA4) grid boxes in which the shape parameter corresponds to a heavy tail. This means that the distributions of maxima of sub-daily amounts are distorted in the RCM-simulated data and do not match reality well. Therefore, projected changes of sub-daily precipitation extremes in climate change scenarios based on RCMs not resolving convection need to be interpreted with caution.

An Automated Technique for Estimating Daily Precipitation over the State of Virginia

NASA Technical Reports Server (NTRS)

Follansbee, W. A.; Chamberlain, L. W., III

1981-01-01

Digital IR and visible imagery obtained from a geostationary satellite located over the equator at 75 deg west latitude were provided by NASA and used to obtain a linear relationship between cloud top temperature and hourly precipitation. Two computer programs written in FORTRAN were used. The first program computes the satellite estimate field from the hourly digital IR imagery. The second program computes the final estimate for the entire state area by comparing five preliminary estimates of 24 hour precipitation with control raingage readings and determining which of the five methods gives the best estimate for the day. The final estimate is then produced by incorporating control gage readings into the winning method. In presenting reliable precipitation estimates for every cell in Virginia in near real time on a daily on going basis, the techniques require on the order of 125 to 150 daily gage readings by dependable, highly motivated observers distributed as uniformly as feasible across the state.

Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

NASA Technical Reports Server (NTRS)

Bolvin, David T.; Adler, Robert G.; Nelkin, Eric J.; Poutiainen, Jani

2008-01-01

It is very important to know how much rain and snow falls around the world for uses that range from crop forecasting to disaster response, drought monitoring to flood forecasting, and weather analysis to climate research. Precipitation is usually measured with rain gauges, but rain gauges don t exist in areas that are sparsely populated, which tends to be a good portion of the globe. To overcome this, meteorologists use satellite data to estimate global precipitation. However, it is difficult to estimate rain and especially snow in cold climates using most current satellites. The satellite sensors are often "confused" by a snowy or frozen surface and therefore cannot distinguish precipitation. One commonly used satellite-based precipitation data set, the Global Precipitation Climatology Project (GPCP) data, overcomes this frozen-surface problem through the innovative use of two sources of satellite data, the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). Though the GPCP estimates are generally considered a very reliable source of precipitation, it has been difficult to assess the quality of these estimates in cold climates due to the lack of gauges. Recently, the Finnish Meteorological Institute (FMI) has provided a 12-year span of high-quality daily rain gauge observations, covering all of Finland, that can be used to compare with the GPCP data to determine how well the satellites estimate cold-climate precipitation. Comparison of the monthly GPCP satellite-based estimates and the FMI gauge observations shows remarkably good agreement, with the GPCP estimates being 6% lower in the amount of precipitation than the FMI observations. Furthermore, the month-to-month correlation between the GPCP and FMI is very high at 0.95 (1.0 is perfect). The daily GPCP estimates replicate the FMI daily occurrences of precipitation with a correlation of 0.55 in the summer and 0.45 in the winter. The winter

Regional variability of the frequency distribution of daily precipitation and the synoptic characteristics of heavy precipitation events in present and future climate simulations

NASA Astrophysics Data System (ADS)

DeAngelis, Anthony M.

Changes in the characteristics of daily precipitation in response to global warming may have serious impacts on human life and property. An analysis of precipitation in climate models is performed to evaluate how well the models simulate the present climate and how precipitation may change in the future. Models participating in phase 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) have substantial biases in their simulation of heavy precipitation intensity over parts of North America during the 20th century. Despite these biases, the large-scale atmospheric circulation accompanying heavy precipitation is either simulated realistically or the strength of the circulation is overestimated. The biases are not related to the large-scale flow in a simple way, pointing toward the importance of other model deficiencies, such as coarse horizontal resolution and convective parameterizations, for the accurate simulation of intense precipitation. Although the models may not sufficiently simulate the intensity of precipitation, their realistic portrayal of the large-scale circulation suggests that projections of future precipitation may be reliable. In the CMIP5 ensemble, the distribution of daily precipitation is projected to undergo substantial changes in response to future atmospheric warming. The regional distribution of these changes was investigated, revealing that dry days and days with heavy-extreme precipitation are projected to increase at the expense of light-moderate precipitation over much of the middle and low latitudes. Such projections have serious implications for future impacts from flood and drought events. In other places, changes in the daily precipitation distribution are characterized by a shift toward either wetter or drier conditions in the future, with heavy-extreme precipitation projected to increase in all but the driest subtropical subsidence regions. Further analysis shows that increases in heavy precipitation in midlatitudes

Trends and variability of daily temperature and precipitation extremes during 1960-2012 in the Yangtze River Basin, China

NASA Astrophysics Data System (ADS)

Guan, Yinghui

2017-04-01

The variability of surface air temperature and precipitation extremes has been the focus of attention during the past several decades, and may exert a great influence on the global hydrologic cycle and energy balance through thermal forcing. Using daily minimum (TN), maximum temperature (TX) and precipitation　from 143 meteorological stations in the Yangtze River Basin (YRB), a suite of extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices, which has rarely been applied in this region, were computed and analyzed during 1960-2012. The results show widespread significant changes in all temperature indices associated with warming in the YRB during 1960-2012. On the whole, cold-related indices, i.e., cold nights, cold days, frost days, icing days and cold spell duration index significantly decreased by -3.45, -1.03, -3.04, -0.42 and -1.6 days/decade, respectively. In contrast, warm-related indices such as warm nights, warm days, summer days, tropical nights and warm spell duration index significantly increased by 2.95, 1.71, 2.16, 1.05 and 0.73 days/decade. Minimum TN, maximum TN, minimum TX and maximum TX increased significantly by 0.42, 0.18, 0.19 and 0.14 °C/decade. Because of a faster increase in minimum temperature than maximum temperature, the diurnal temperature range (DTR) exhibited a significant decreasing trend of -0.09 °C/decade for the whole YRB during 1960-2012. Geographically, stations in the eastern Tibet Plateau and northeastern YRB showed stronger trends in almost all temperature indices. Time series analysis indicated that the YRB was dominated by a general cooling trend before the mid-1980s, but a warming trend afterwards. For precipitation, simple daily intensity index, very wet day precipitation, extremely wet day precipitation, extremely heavy precipitation days, maximum 1-day precipitation, maximum 5-day precipitation and maximum consecutive dry days all increased significantly during 1960-2012. In

A data centred method to estimate and map how the local distribution of daily precipitation is changing

NASA Astrophysics Data System (ADS)

Chapman, Sandra; Stainforth, David; Watkins, Nick

2014-05-01

Estimates of how our climate is changing are needed locally in order to inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles in distributions of variables such as daily temperature or precipitation. Here we focus on these local changes and on a method to transform daily observations of precipitation into patterns of local climate change. We develop a method[1] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes, to specifically address the challenges presented by daily precipitation data. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the relative amount of precipitation in those days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily precipitation from specific locations across Europe over the last 60 years. We treat geographical location and precipitation as independent variables and thus obtain as outputs the pattern of change at a given threshold of precipitation and with geographical location. This is model- independent, thus providing data of direct value in model calibration and assessment. Our results show regionally consistent patterns of systematic increase in precipitation on the wettest days, and of drying across all days which is of potential value in

Trends and variability of daily precipitation extremes during 1960-2012 in the Yangtze River Basin, China

USDA-ARS?s Scientific Manuscript database

Trends and variability of extreme precipitation events are important for water-related disaster prevention and mitigation as well as water resource management. Based on daily precipitation dataset from 143 meteorological stations in the Yangtze River Basin (YRB), a suite of precipitation indices rec...

Quantifying how the full local distribution of daily precipitation is changing and its uncertainties

NASA Astrophysics Data System (ADS)

Stainforth, David; Chapman, Sandra; Watkins, Nicholas

2016-04-01

The study of the consequences of global warming would benefit from quantification of geographical patterns of change at specific thresholds or quantiles, and better understandings of the intrinsic uncertainties in such quantities. For precipitation a range of indices have been developed which focus on high percentiles (e.g. rainfall falling on days above the 99th percentile) and on absolute extremes (e.g. maximum annual one day precipitation) but scientific assessments are best undertaken in the context of changes in the whole climatic distribution. Furthermore, the relevant thresholds for climate-vulnerable policy decisions, adaptation planning and impact assessments, vary according to the specific sector and location of interest. We present a methodology which maintains the flexibility to provide information at different thresholds for different downstream users, both scientists and decision makers. We develop a method[1,2] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes in daily precipitation data. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the amount of precipitation on those days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves not only determining which quantiles and geographical locations show the greatest and smallest changes, but also those at which uncertainty undermines the ability to make confident statements about any change there may be. We demonstrate this approach using E-OBS gridded data[3] which are timeseries of local daily

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also

PERSIANN-CDR Daily Precipitation Dataset for Hydrologic Applications and Climate Studies.

NASA Astrophysics Data System (ADS)

Sorooshian, S.; Hsu, K. L.; Ashouri, H.; Braithwaite, D.; Nguyen, P.; Thorstensen, A. R.

2015-12-01

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record (PERSIANN-CDR) is a newly developed and released dataset which covers more than 3 decades (01/01/1983 - 03/31/2015 to date) of daily precipitation estimations at 0.25° resolution for 60°S-60°N latitude band. PERSIANN-CDR is processed using the archive of the Gridded Satellite IRWIN CDR (GridSat-B1) from the International Satellite Cloud Climatology Project (ISCCP), and the Global Precipitation Climatology Project (GPCP) 2.5° monthly product for bias correction. The dataset has been released and made available for public access through NOAA's National Centers for Environmental Information (NCEI) (http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/PERSIANN/Overview.pdf). PERSIANN-CDR has already shown its usefulness for a wide range of applications, including climate variability and change monitoring, hydrologic applications, and water resources system planning and management. This precipitation CDR data has also been used in studying the behavior of historical extreme precipitation events. Demonstration of PERSIANN-CDR data in detecting trends and variability of precipitation over the past 30 years, the potential usefulness of the dataset for evaluating climate model performance relevant to precipitation in retrospective mode, will be presented.

Homogeneity revisited: analysis of updated precipitation series in Turkey

NASA Astrophysics Data System (ADS)

Bickici Arikan, Bugrayhan; Kahya, Ercan

2018-01-01

Homogeneous time series of meteorological variables are necessary for hydrologic and climate studies. Dependability of historical precipitation data is subjected to keen evaluation prior to every study in water resources, hydrology, and climate change fields. This study aims to characterize the homogeneity of long-term Turkish precipitation data in order to ensure that they can be reliably used. The homogeneity of monthly precipitation data set was tested using the standard normal homogeneity test, Buishand test, Von Neumann ratio test, and Pettitt test at the 5% significance level across Turkey. Our precipitation records including the most updated observations, extracted from 160 meteorological stations, for the periods 1974-2014 were analyzed by all the four homogeneity tests. According to the results of all tests, five out of 160 stations have an inhomogeneity. With regard to our strict confirmation rule, 44 out of 160 stations are said to be inhomogeneous since they failed from at least one of the four tests. The breaks captured by the Buishand and Pettitt tests usually tend to appear in the middle of the precipitation series, whereas the ability of standard normal homogeneity test is in favor of identifying inhomogeneities mostly at the beginning or at the end of the records. Our results showed that 42 out of 44 inhomogeneous stations passed all the four tests after applying a correction procedure based on the double mass curve analysis. Available metadata was used to interpret the detected inhomogeneity.

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed.

PubMed

Feng, Zhujing; Schilling, Keith E; Chan, Kung-Sik

2013-06-01

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

USDA-ARS?s Scientific Manuscript database

Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

USDA-ARS?s Scientific Manuscript database

Missing meteorological data have to be estimated for agricultural and environmental modeling. The objective of this work was to develop a technique to reconstruct the missing daily precipitation data in the central part of the Chesapeake Bay Watershed using regression trees (RT) and artificial neura...

User's Guide, software for reduction and analysis of daily weather and surface-water data: Tools for time series analysis of precipitation, temperature, and streamflow data

USGS Publications Warehouse

Hereford, Richard

2006-01-01

The software described here is used to process and analyze daily weather and surface-water data. The programs are refinements of earlier versions that include minor corrections and routines to calculate frequencies above a threshold on an annual or seasonal basis. Earlier versions of this software were used successfully to analyze historical precipitation patterns of the Mojave Desert and the southern Colorado Plateau regions, ecosystem response to climate variation, and variation of sediment-runoff frequency related to climate (Hereford and others, 2003; 2004; in press; Griffiths and others, 2006). The main program described here (Day_Cli_Ann_v5.3) uses daily data to develop a time series of various statistics for a user specified accounting period such as a year or season. The statistics include averages and totals, but the emphasis is on the frequency of occurrence in days of relatively rare weather or runoff events. These statistics are indices of climate variation; for a discussion of climate indices, see the Climate Research Unit website of the University of East Anglia (http://www.cru.uea.ac.uk/projects/stardex/) and the Climate Change Indices web site (http://cccma.seos.uvic.ca/ETCCDMI/indices.html). Specifically, the indices computed with this software are the frequency of high intensity 24-hour rainfall, unusually warm temperature, and unusually high runoff. These rare, or extreme events, are those greater than the 90th percentile of precipitation, streamflow, or temperature computed for the period of record of weather or gaging stations. If they cluster in time over several decades, extreme events may produce detectable change in the physical landscape and ecosystem of a given region. Although the software has been tested on a variety of data, as with any software, the user should carefully evaluate the results with their data. The programs were designed for the range of precipitation, temperature, and streamflow measurements expected in the semiarid

Precipitation trends in the Canary Islands

NASA Astrophysics Data System (ADS)

García-Herrera, Ricardo; Gallego, David; Hernández, Emiliano; Gimeno, Luis; Ribera, Pedro; Calvo, Natalia

2003-02-01

A strong decreasing trend in the Canary Islands' precipitation is detected by studying daily rainfall time series for the second half of the 20th century. An analysis of the extreme events shows that this trend is due mainly to a decrease in the upper percentiles of the precipitation distribution. The results suggest that local factors play a fundamental role on extreme event behaviour.

Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

NASA Astrophysics Data System (ADS)

Wong, Jefferson S.; Razavi, Saman; Bonsal, Barrie R.; Wheater, Howard S.; Asong, Zilefac E.

2017-04-01

A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.

A quasi-global precipitation time series for drought monitoring

USGS Publications Warehouse

Funk, Chris C.; Peterson, Pete J.; Landsfeld, Martin F.; Pedreros, Diego H.; Verdin, James P.; Rowland, James D.; Romero, Bo E.; Husak, Gregory J.; Michaelsen, Joel C.; Verdin, Andrew P.

2014-01-01

Estimating precipitation variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in historical context so that the severity of rainfall deficits may quickly be evaluated. To this end, scientists at the U.S. Geological Survey Earth Resources Observation and Science Center, working closely with collaborators at the University of California, Santa Barbara Climate Hazards Group, have developed a quasi-global (50°S–50°N, 180°E–180°W), 0.05° resolution, 1981 to near-present gridded precipitation time series: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data archive.

Frequency Analysis of Extreme Sub-Daily Precipitation under Stationary and Non-Stationary Conditions across Two Contrasting Hydroclimatic Environments

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Goodrich, D. C.; Keefer, T.

2017-12-01

Observed sub-daily precipitation intensities from contrasting hydroclimatic environments in the USA are used to evaluate temporal trends and to develop Intensity-Duration Frequency (IDF) curves under stationary and nonstationary climatic conditions. Analyses are based on observations from two United States Department of Agriculture (USDA)-Agricultural Research Service (ARS) experimental watersheds located in a semi-arid and a temperate environment. We use an Annual Maximum Series (AMS) and a Partial Duration Series (PDS) approach to identify temporal trends in maximum intensities for durations ranging from 5- to 1440-minutes. A Bayesian approach with Monte Carlo techniques is used to incorporate the effect of non-stationary climatic assumptions in the IDF curves. The results show increasing trends in observed AMS sub-daily intensities in both watersheds whereas trends in the PDS observations are mostly positive in the semi-arid site and a mix of positive and negative in the temperate site. Stationary climate assumptions lead to much lower estimated sub-daily intensities than those under non-stationary assumptions with larger absolute differences found for shorter durations and smaller return periods. The risk of failure (R) of a hydraulic structure is increased for non-stationary effects over those of stationary effects, with absolute differences of 25% for a 100-year return period (T) and a project life (n) of 100 years. The study highlights the importance of considering non-stationarity, due to natural variability or to climate change, in storm design.

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

NASA Astrophysics Data System (ADS)

Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi

2017-12-01

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation

Seasonal and annual precipitation time series trend analysis in North Carolina, United States

NASA Astrophysics Data System (ADS)

Sayemuzzaman, Mohammad; Jha, Manoj K.

2014-02-01

The present study performs the spatial and temporal trend analysis of the annual and seasonal time-series of a set of uniformly distributed 249 stations precipitation data across the state of North Carolina, United States over the period of 1950-2009. The Mann-Kendall (MK) test, the Theil-Sen approach (TSA) and the Sequential Mann-Kendall (SQMK) test were applied to quantify the significance of trend, magnitude of trend, and the trend shift, respectively. Regional (mountain, piedmont and coastal) precipitation trends were also analyzed using the above-mentioned tests. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation of precipitation data series. The application of the above-mentioned procedures has shown very notable statewide increasing trend for winter and decreasing trend for fall precipitation. Statewide mixed (increasing/decreasing) trend has been detected in annual, spring, and summer precipitation time series. Significant trends (confidence level ≥ 95%) were detected only in 8, 7, 4 and 10 nos. of stations (out of 249 stations) in winter, spring, summer, and fall, respectively. Magnitude of the highest increasing (decreasing) precipitation trend was found about 4 mm/season (- 4.50 mm/season) in fall (summer) season. Annual precipitation trend magnitude varied between - 5.50 mm/year and 9 mm/year. Regional trend analysis found increasing precipitation in mountain and coastal regions in general except during the winter. Piedmont region was found to have increasing trends in summer and fall, but decreasing trend in winter, spring and on an annual basis. The SQMK test on "trend shift analysis" identified a significant shift during 1960 - 70 in most parts of the state. Finally, the comparison between winter (summer) precipitations with the North Atlantic Oscillation (Southern Oscillation) indices concluded that the variability and trend of precipitation can be explained by the

An objective daily Weather Type classification for Iberia since 1850; patterns, trends, variability and impact in precipitation

NASA Astrophysics Data System (ADS)

Ramos, A. M.; Trigo, R. M.; Lorenzo, M. N.; Vaquero, J. M.; Gallego, M. C.; Valente, M. A.; Gimeno, L.

2009-04-01

In recent years a large number of automated classifications of atmospheric circulation patterns have been published covering the entire European continent or specific sub-regions (Huth et al., 2008). This generalized use of objective classifications results from their relatively straightforward computation but crucially from their capacity to provide simple description of typical synoptic conditions as well as their climatic and environmental impact. For this purpose, the vast majority of authors has employed the Reanalyses datasets, namely from either NCEP/NCAR or ECMWF projects. However, both these widely used datasets suffer from important caveats, namely their restricted temporal coverage, that is limited to the last six decades (NCEP/NCAR since 1948 and ECMWF since 1958). This limitation has been partially mitigated by the recent availability of continuous daily mean sea level pressure obtained within the European project EMULATE, that extended the historic records over the extra-tropical Atlantic and Europe (70°-25° N by 70° W-50° E), for the period 1850 to the present (Ansell, T. J. et al. 2006). Here we have used the extended EMULATE dataset to construct an automated version of the Lamb Weather type (WTs) classification scheme (Jones et al 1993) adapted for the center of the Iberian Peninsula. We have identified 10 basic WTs (Cyclonic, Anticyclonic and 8 directional types) following a similar methodology to that previously adopted by Trigo and DaCamara, 2000 (for Portugal) and Lorenzo et al. 2008 (for Galicia, northwestern Iberia). We have evaluated trends of monthly/seasonal frequency of each WT for the entire period and several shorter periods. Finally, we use the long-term precipitation time series from Lisbon (recently digitized) and Cadiz (southern Spain) to evaluate, the impact of each WT on the precipitation regime. It is shown that the Anticyclonic (A) type, although being the most frequent class in winter, gives a rather small contribution to

Daily precipitation extreme events for the Iberian Peninsula and its association with Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Ramos, Alexandre M.; Trigo, Ricardo M.; Liberato, Margarida LR

2014-05-01

Extreme precipitation events in the Iberian Peninsula during the extended winter months have major socio-economic impacts such as floods, landslides, extensive property damage and life losses. These events are usually associated with low pressure systems with Atlantic origin, although some extreme events in summer/autumn months can be linked to Mediterranean low pressure systems. Quite often these events are evaluated on a casuistic base and making use of data from relatively few stations. An objective method for ranking daily precipitation events is presented here based on the extensive use of the most comprehensive database of daily gridded precipitation available for the Iberian Peninsula (IB02) and spanning from 1950 to 2008, with a resolution of 0.2° (approximately 16 x 22 km at latitude 40°N), for a total of 1673 pixels. This database is based on a dense network of rain gauges, combining two national data sets, 'Spain02' for peninsular Spain and Balearic islands, and 'PT02' for mainland Portugal, with a total of more than two thousand stations over Spain and four hundred stations over Portugal, all quality-controlled and homogenized. Through this objective method for ranking daily precipitation events the magnitude of an event is obtained after considering the area affected as well as its intensity in every grid point and taking into account the daily precipitation normalised departure from climatology. Different precipitation rankings are presented considering the entire Iberian Peninsula, Portugal and also the six largest river basins in the Iberian Peninsula. Atmospheric Rivers (AR) are the water vapour (WV) core section of the broader warm conveyor belt occurring over the oceans along the warm sector of extra-tropical cyclones. They are usually W-E oriented steered by pre-frontal low level jets along the trailing cold front and subsequently feed the precipitation in the extra-tropical cyclones. They are relatively narrow regions of concentrated WV

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

PubMed

Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

2013-01-01

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

The changes in the frequency of daily precipitation in Urmia Lake basin, Iran

NASA Astrophysics Data System (ADS)

Salehi Bavil, Sepideh; Zeinalzadeh, Kamran; Hessari, Behzad

2017-06-01

Urmia Lake, as one of the most valuable saline ecosystems in the world, has faced a sharp drop in the water level in recent years. The trend studies of climatic parameters can be effective in identifying the responsible factors and managing this crisis. This research investigated the frequency trend of daily precipitation in the ranges of less than 5 mm, 5-10 mm, 10-15 mm, 15-20 mm, and more than 20 mm in the Urmia Lake basin. The trend was assessed using Mann-Kendall, Spearman Rho and linear regression tests on 60 stations during a period of 30 years (1981 to 2011). The results showed that in all the three tests, the frequency of daily precipitation of less than 5 mm had a significant increase at 1% level. The 5-10 mm range displayed no significant trend, while the 10-15 mm range showed a significantly decreasing trend. The frequency in the 15-20 mm and above 20 mm ranges showed an insignificant falling trend. The analysis also indicated jumps in 1996 and 1999 (almost coinciding with the sharp drop in the lake's water level). In other words, the frequency trends of daily precipitation with small amounts (as a result, high evapotranspiration loss) were increasing and with large amounts were decreasing. This can be a contributor to reduced run-off and, hence, decreased water entering the lake. The results emphasize the need for changes in the management and consumption of water resources in the basin, in order to adapt to the climatic change.

Effects of daily precipitation and evapotranspiration patterns on flow and VOC transport to groundwater along a watershed flow path

USGS Publications Warehouse

Johnson, Richard L.; Thoms, R.B.; Zogorski, J.S.

2003-01-01

MTBE and other volatile organic compounds (VOCs) are widely observed in shallow groundwater in the United States, especially in urban areas. Previous studies suggest that the atmosphere and/or nonpoint surficial sources could be responsible for some of those VOCs, especially in areas where there is net recharge to groundwater. However, in semi-arid locations where annual potential evapotranspiration can exceed annual precipitation, VOC detections in groundwater can be frequent. VOC transport to groundwater under net discharge conditions has not previously been examined. A numerical model is used here to demonstrate that daily precipitation and evapotranspiration (ET) patterns can have a significant effect on recharge to groundwater, water table elevations, and VOC transport. Ten-year precipitation/ET scenarios from six sites in the United States are examined using both actual daily observed values and “average” pulsed precipitation. MTBE and tetrachloroethylene transport, including gas-phase diffusion, are considered. The effects of the precipitation/ET scenarios on net recharge and groundwater flow are significant and complicated, especially under low-precipitation conditions when pulsed precipitation can significantly underestimate transport to groundwater. In addition to precipitation and evapotranspiration effects, location of VOC entry into the subsurface within the watershed is important for transport in groundwater. This is caused by groundwater hydraulics at the watershed scale as well as variations in ET within the watershed. The model results indicate that it is important to consider both daily precipitation/ET patterns and location within the watershed in order to interpret VOC occurrence in groundwater, especially in low-precipitation settings.

Incorporating TRMM and Other High-Quality Estimates into the One-Degree Daily (1DD) Global Precipitation Product

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.

1999-01-01

The One-Degree Daily (1DD) precipitation dataset was recently developed for the Global Precipitation Climatology Project (GPCP). The IDD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). In the latitude band 40 N - 40 S the IDD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSNU product and by the precipitation amount in the GPCP satellite-gauge (SG) combination. Outside 40 N - 40 S the 1DD uses a scaled TOVS precipitation estimate that has adjustments based on the TMPI and the SG. This first-generation 1DD has been in beta test preparatory to release as an official GPCP product. In this paper we discuss further development of the 1DD framework to allow the direct incorporation of TRMM and other high-quality precipitation estimates. First, these data are generally sparse (typically from low-orbit satellites), so a fair amount of work was devoted to data boundaries. Second, these data are not the same as the original 1DD estimates, so we had to give careful consideration to the best scheme for forcing the 1DD to sum to the SG for the month. Finally, the non-sun-synchronous, low-inclination orbit occupied by TRMM creates interesting variations against the sun-synchronous, high-inclination orbits occupied by the Defense Meteorological Satellite Program satellites that carry the SSM/I. Examples will be given of each of the development issues, then comparisons will be made to daily raingauge analyses.

CPLFD-GDPT5: high-resolution gridded daily precipitation and temperature dataset for two largest Polish river basins

NASA Astrophysics Data System (ADS)

Berezowski, T.; Szcześniak, M.; Kardel, I.; Michałowski, R.; Okruszko, T.; Mezghani, A.; Piniewski, M.

2015-12-01

The CHASE-PL Forcing Data-Gridded Daily Precipitation and Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951-2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), ECAD and NOAA-NCDC (Slovak, Ukrainian and Belarus stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of Vistula and Odra basin and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in 1950 up to about 180 for temperature and 700 for precipitation in 1990. The precipitation dataset was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were: kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross-validation revealed low root mean squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971-2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Odra basins. Link to the dataset: http://data.3tu.nl/repository/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07

Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations

NASA Technical Reports Server (NTRS)

Gutowski, William J.; Lindemulder, Elizabeth A.; Jovaag, Kari

1995-01-01

We use retrievals of atmospheric precipitable water from satellite microwave observations and analyses of near-surface temperature to examine the relationship between these two fields on daily and longer time scales. The retrieval technique producing the data used here is most effective over the open ocean, so the analysis focuses on the southern hemisphere's extratropics, which have an extensive ocean surface. For both the total and the eddy precipitable water fields, there is a close correspondence between local variations in the precipitable water and near-surface temperature. The correspondence appears particularly strong for synoptic and planetary scale transient eddies. More specifically, the results support a typical modeling assumption that transient eddy moisture fields are proportional to transient eddy temperature fields under the assumption f constant relative humidity.

The creation of future daily gridded datasets of precipitation and temperature with a spatial weather generator, Cyprus 2020-2050

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred

2014-05-01

High-resolution gridded daily datasets are essential for natural resource management and the analysis of climate changes and their effects. This study aimed to create gridded datasets of daily precipitation and daily minimum and maximum temperature, for the future (2020-2050). The horizontal resolution of the developed datasets is 1 x 1 km2, covering the area under control of the Republic of Cyprus (5.760 km2). The study is divided into two parts. The first consists of the evaluation of the performance of different interpolation techniques for daily rainfall and temperature data (1980-2010) for the creation of the gridded datasets. Rainfall data recorded at 145 stations and temperature data from 34 stations were used. For precipitation, inverse distance weighting (IDW) performs best for local events, while a combination of step-wise geographically weighted regression and IDW proves to be the best method for large scale events. For minimum and maximum temperature, a combination of step-wise linear multiple regression and thin plate splines is recognized as the best method. Six Regional Climate Models (RCMs) for the A1B SRES emission scenario from the EU ENSEMBLE project database were selected as sources for future climate projections. The RCMs were evaluated for their capacity to simulate Cyprus climatology for the period 1980-2010. Data for the period 2020-2050 from the three best performing RCMs were downscaled, using the change factors approach, at the location of observational stations. Daily time series were created with a stochastic rainfall and temperature generator. The RainSim V3 software (Burton et al., 2008) was used to generate spatial-temporal coherent rainfall fields. The temperature generator was developed in R and modeled temperature as a weakly stationary process with the daily mean and standard deviation conditioned on the wet and dry state of the day (Richardson, 1981). Finally gridded datasets depicting projected future climate conditions were

A Data Centred Method to Estimate and Map Changes in the Full Distribution of Daily Precipitation and Its Exceedances

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Stainforth, D. A.; Watkins, N. W.

2014-12-01

Estimates of how our climate is changing are needed locally in order to inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles or thresholds in distributions of variables such as daily temperature or precipitation. We develop a method[1] for analysing local climatic timeseries to assess which quantiles of the local climatic distribution show the greatest and most robust changes, to specifically address the challenges presented by 'heavy tailed' distributed variables such as daily precipitation. We extract from the data quantities that characterize the changes in time of the likelihood of daily precipitation above a threshold and of the relative amount of precipitation in those extreme precipitation days. Our method is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of precipitation distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. We demonstrate this approach using E-OBS gridded data[2] timeseries of local daily precipitation from specific locations across Europe over the last 60 years. We treat geographical location and precipitation as independent variables and thus obtain as outputs the pattern of change at a given threshold of precipitation and with geographical location. This is model- independent, thus providing data of direct value in model calibration and assessment. Our results identify regionally consistent patterns which, dependent on location, show systematic increase in precipitation on the wettest days, shifts in precipitation patterns to less moderate days and more heavy days, and drying

33 Years of Near-Global Daily Precipitation from Multisatellite Observations and its Application to Drought Monitoring

NASA Astrophysics Data System (ADS)

Ashouri, H.; Hsu, K.; Sorooshian, S.; Braithwaite, D.; Knapp, K. R.; Cecil, L. D.

2013-12-01

PERSIANN Climate Data Record (PERSIANN-CDR) is a new retrospective satellite-based precipitation data set that is constructed for long-term hydrological and climate studies. The PERSIANN-CDR is a near-global (60°S-60°N) long-term (1980-2012), multi-satellite, high-resolution precipitation product that provides rain rate estimates at 0.25° and daily spatiotemporal resolution. PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high resolution precipitation data set for studying the spatial and temporal variations and changes of precipitation patterns, particularly in a scale relevant to climate extremes at the global scale. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data from the International Satellite Cloud Climatology Project (ISCCP). PERSIANN-CDR is adjusted using the Global Precipitation Climatology Project (GPCP) monthly precipitation to maintain consistency of two data sets at 2.5° monthly scale throughout the entire reconstruction period. PERSIANN-CDR daily precipitation data demonstrates considerable consistency with both GPCP monthly and GPCP 1DD precipitation products. Verification studies over Hurricane Katrina show that PERSIANN-CDR has a good agreement with NCEP Stage IV radar data, noting that PERSIANN-CDR has better spatial coverage. In addition, the Probability Density Function (PDF) of PERSIANN-CDR over the contiguous United States was compared with the PDFs extracted from CPC gauge data and the TMPA precipitation product. The experiment also shows good agreement of the PDF of PERSIANN-CDR with the PDFs of TMPA and CPC gauge data. The application of PERSIANN-CDR in regional and global drought monitoring is investigated. Consisting of more than three decades of high-resolution precipitation data, PERSIANN-CDR makes us capable of long-term assessment of droughts at a higher resolution (0.25°) than previously possible. The results will be presented at the meeting.

A 47-Year Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges -APHRODITE project-

NASA Astrophysics Data System (ADS)

Yatagai, A. I.; Yasutomi, N.; Hamada, A.; Kamiguchi, K.; Arakawa, O.

2009-12-01

A daily gridded precipitation dataset for 1961-2007 is created by collecting rain gauge observation data across Asia through the activities of the Asian Precipitation--Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) project. We have already released APHRODITE’s daily gridded precipitation (APHRO_V0902) product for 1961-2004 (Yatagai et al., 2009), and our number of valid stations was between 5000 and 12,000, representing 2.3 to 4.5 times the data available through the Global Telecommunication System network, which were used for most daily grid precipitation products. APHRO_V0902 is the only long-term (1961 onward) continental-scale daily product that contains a dense network of daily rain gauge data for Asia including the Himalayas and mountainous areas in the Middle East. The product has already contributed to studies such as the evaluation of Asian water resources, diagnosis of climate change, statistical downscaling, and verification of numerical model simulation and high-resolution precipitation estimates using satellites. We are currently improving quality control (QC) schemes and interpolation algorithms, and make continuous efforts in data collection. In addition, we have undertaken capacity building activities, such as training seminars by inviting researchers/programmers from some Asian meteorological organizations who provided the observation data for us. Furthermore, we feed the errata (QC) information back to the above organizations and/or data centers. The next version of the algorithm will be fixed in December 2009 (APHRO_V0912), and we will update the product up to 2007. Our progress and advantage of the next products will be shown at the AGU fall meeting in 2009.

Areal and Temporal Analysis of Precipitation Patterns In Slovakia Using Spectral Analysis

NASA Astrophysics Data System (ADS)

Pishvaei, M. R.

Harmonic analysis as an objective method of precipitation seasonality studying is ap- plied to the 1901-2000 monthly precipitation averages at five stations in the low-land part of Slovakia with elevation less than 800 m a.s.l. The significant harmonics of long-term precipitation series have been separately computed for eight 30-year peri- ods, which cover the 20th century and some properties and the variations are com- pared to 100-year monthly precipitation averages. The selected results show that the first and the second harmonics pre-dominantly influence on the annual distribution and climatic seasonal regimes of pre-cipitation that contribute to the precipitation am- plitude/pattern with about 20% and 10%, respectively. These indicate annual and half year variations. The rest harmon-ics often have each less than 5% contribution on the Fourier interpolation course. Maximum in yearly precipitation course, which oc- curs approximately at the begin-ning of July, because of phase changing shifts then to the middle of June. Some probable reasons regarding to Fourier components are discussed. In addition, a tem-poral analysis over precipitation time series belonging to the Hurbanovo Observa-tory as the longest observational series on the territory of Slovakia (with 130-year precipitation records) has been individually performed and possible meteorological factors responsible for the observed patterns are suggested. A comparison of annual precipitation course obtained from daily precipitation totals analysis and polynomial trends with Fourier interpolation has been done too. Daily precipitation data in the latest period are compared for some stations in Slovakia as well. Only selected results are pre-sented in the poster.

Improved daily precipitation nitrate and ammonium concentration models for the Chesapeake Bay Watershed.

PubMed

Grimm, J W; Lynch, J A

2005-06-01

Daily precipitation nitrate and ammonium concentration models were developed for the Chesapeake Bay Watershed (USA) using a linear least-squares regression approach and precipitation chemistry data from 29 National Atmospheric Deposition Program/National Trends Network (NADP/NTN) sites. Only weekly samples that comprised a single precipitation event were used in model development. The most significant variables in both ammonium and nitrate models included: precipitation volume, the number of days since the last event, a measure of seasonality, latitude, and the proportion of land within 8km covered by forest or devoted to industry and transportation. Additional variables included in the nitrate model were the proportion of land within 0.8km covered by water and/or forest. Local and regional ammonia and nitrogen oxide emissions were not as well correlated as land cover. Modeled concentrations compared very well with event chemistry data collected at six NADP/AirMoN sites within the Chesapeake Bay Watershed. Wet deposition estimates were also consistent with observed deposition at selected sites. Accurately describing the spatial distribution of precipitation volume throughout the watershed is important in providing critical estimates of wet-fall deposition of ammonium and nitrate.

Circulation controls of the spatial structure of maximum daily precipitation over Poland

NASA Astrophysics Data System (ADS)

Stach, Alfred

2015-04-01

Among forecasts made on the basis of global and regional climatic models is one of a high probability of an increase in the frequency and intensity of extreme precipitation events. Learning the regularities underlying the recurrence and spatial extent of extreme precipitation is obviously of great importance, both economic and social. The main goal of the study was to analyse regularities underlying spatial and temporal variations in monthly Maximum Daily Precipitation Totals (MDPTs) observed in Poland over the years 1956-1980. These data are specific because apart from being spatially discontinuous, which is typical of precipitation, they are also non-synchronic. The main aim of the study was accomplished via several detailed goals: • identification and typology of the spatial structure of monthly MDPTs, • determination of the character and probable origin of events generating MDPTs, and • quantitative assessment of the contribution of the particular events to the overall MDPT figures. The analysis of the spatial structure of MDPTs was based on 300 models of spatial structure, one for each of the analysed sets of monthly MDPTs. The models were built on the basis of empirical anisotropic semivariograms of normalised data. In spite of their spatial discontinuity and asynchronicity, the MDPT data from Poland display marked regularities in their spatial pattern that yield readily to mathematical modelling. The MDPT field in Poland is usually the sum of the outcomes of three types of processes operating at various spatial scales: local (<10-20 km), regional (50-150 km), and supra-regional (>200 km). The spatial scales are probably connected with a convective/ orographic, a frontal and a 'planetary waves' genesis of high precipitation. Their contributions are highly variable. Generally predominant, however, are high daily precipitation totals with a spatial extent of 50 to 150 km connected with mesoscale phenomena and the migration of atmospheric fronts (35

Spatial distribution of precipitation extremes in Norway

NASA Astrophysics Data System (ADS)

Verpe Dyrrdal, Anita; Skaugen, Thomas; Lenkoski, Alex; Thorarinsdottir, Thordis; Stordal, Frode; Førland, Eirik J.

2015-04-01

Estimates of extreme precipitation, in terms of return levels, are crucial in planning and design of important infrastructure. Through two separate studies, we have examined the levels and spatial distribution of daily extreme precipitation over catchments in Norway, and hourly extreme precipitation in a point. The analyses were carried out through the development of two new methods for estimating extreme precipitation in Norway. For daily precipitation we fit the Generalized Extreme Value (GEV) distribution to areal time series from a gridded dataset, consisting of daily precipitation during the period 1957-today with a resolution of 1x1 km². This grid-based method is more objective and less manual and time-consuming compared to the existing method at MET Norway. In addition, estimates in ungauged catchments are easier to obtain, and the GEV approach includes a measure of uncertainty, which is a requirement in climate studies today. Further, we go into depth on the debated GEV shape parameter, which plays an important role for longer return periods. We show that it varies according to dominating precipitation types, having positive values in the southeast and negative values in the southwest. We also find indications that the degree of orographic enhancement might affect the shape parameter. For hourly precipitation, we estimate return levels on a 1x1 km² grid, by linking GEV distributions with latent Gaussian fields in a Bayesian hierarchical model (BHM). Generalized linear models on the GEV parameters, estimated from observations, are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Gaussian fields capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected, while a Bayesian model averaging component directly assesses model uncertainty. We find that mean summer precipitation, mean summer temperature, latitude

Uncertainty in determining extreme precipitation thresholds

NASA Astrophysics Data System (ADS)

Liu, Bingjun; Chen, Junfan; Chen, Xiaohong; Lian, Yanqing; Wu, Lili

2013-10-01

Extreme precipitation events are rare and occur mostly on a relatively small and local scale, which makes it difficult to set the thresholds for extreme precipitations in a large basin. Based on the long term daily precipitation data from 62 observation stations in the Pearl River Basin, this study has assessed the applicability of the non-parametric, parametric, and the detrended fluctuation analysis (DFA) methods in determining extreme precipitation threshold (EPT) and the certainty to EPTs from each method. Analyses from this study show the non-parametric absolute critical value method is easy to use, but unable to reflect the difference of spatial rainfall distribution. The non-parametric percentile method can account for the spatial distribution feature of precipitation, but the problem with this method is that the threshold value is sensitive to the size of rainfall data series and is subjected to the selection of a percentile thus make it difficult to determine reasonable threshold values for a large basin. The parametric method can provide the most apt description of extreme precipitations by fitting extreme precipitation distributions with probability distribution functions; however, selections of probability distribution functions, the goodness-of-fit tests, and the size of the rainfall data series can greatly affect the fitting accuracy. In contrast to the non-parametric and the parametric methods which are unable to provide information for EPTs with certainty, the DFA method although involving complicated computational processes has proven to be the most appropriate method that is able to provide a unique set of EPTs for a large basin with uneven spatio-temporal precipitation distribution. The consistency between the spatial distribution of DFA-based thresholds with the annual average precipitation, the coefficient of variation (CV), and the coefficient of skewness (CS) for the daily precipitation further proves that EPTs determined by the DFA method

Evolution of precipitation extremes in two large ensembles of climate simulations

NASA Astrophysics Data System (ADS)

Martel, Jean-Luc; Mailhot, Alain; Talbot, Guillaume; Brissette, François; Ludwig, Ralf; Frigon, Anne; Leduc, Martin; Turcotte, Richard

2017-04-01

Recent studies project significant changes in the future distribution of precipitation extremes due to global warming. It is likely that extreme precipitation intensity will increase in a future climate and that extreme events will be more frequent. In this work, annual maxima daily precipitation series from the Canadian Earth System Model (CanESM2) 50-member large ensemble (spatial resolution of 2.8°x2.8°) and the Community Earth System Model (CESM1) 40-member large ensemble (spatial resolution of 1°x1°) are used to investigate extreme precipitation over the historical (1980-2010) and future (2070-2100) periods. The use of these ensembles results in respectively 1 500 (30 years x 50 members) and 1200 (30 years x 40 members) simulated years over both the historical and future periods. These large datasets allow the computation of empirical daily extreme precipitation quantiles for large return periods. Using the CanESM2 and CESM1 large ensembles, extreme daily precipitation with return periods ranging from 2 to 100 years are computed in historical and future periods to assess the impact of climate change. Results indicate that daily precipitation extremes generally increase in the future over most land grid points and that these increases will also impact the 100-year extreme daily precipitation. Considering that many public infrastructures have lifespans exceeding 75 years, the increase in extremes has important implications on service levels of water infrastructures and public safety. Estimated increases in precipitation associated to very extreme precipitation events (e.g. 100 years) will drastically change the likelihood of flooding and their extent in future climate. These results, although interesting, need to be extended to sub-daily durations, relevant for urban flooding protection and urban infrastructure design (e.g. sewer networks, culverts). Models and simulations at finer spatial and temporal resolution are therefore needed.

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

NASA Technical Reports Server (NTRS)

Milesi, Cristina; Costa-Cabral, Mariza; Rath, John; Mills, William; Roy, Sujoy; Thrasher, Bridget; Wang, Weile; Chiang, Felicia; Loewenstein, Max; Podolske, James

2014-01-01

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins

NASA Astrophysics Data System (ADS)

Berezowski, Tomasz; Szcześniak, Mateusz; Kardel, Ignacy; Michałowski, Robert; Okruszko, Tomasz; Mezghani, Abdelkader; Piniewski, Mikołaj

2016-03-01

The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Forcing Data-Gridded Daily Precipitation & Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951-2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from the Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), and European Climate Assessment and Dataset (ECAD) and National Oceanic and Atmosphere Administration-National Climatic Data Center (NOAA-NCDC) (Slovak, Ukrainian, and Belarusian stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of the Vistula and Oder basins and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in the 1950s up to about 180 for temperature and 700 for precipitation in the 1990s. The precipitation data set was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross validation revealed low root-mean-squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively, and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures, and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971-2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in

Which homogenisation method is appropriate for daily time series of relative humidity?

NASA Astrophysics Data System (ADS)

Chimani, Barbara; Nemec, Johanna; Auer, Ingeborg; Venema, Victor

2014-05-01

Data homogenisation is an essential part of reliable climate data analyses. Different tools for detecting and adjusting breaks in daily extreme temperatures (Tmin, Tmax) and daily precipitation sums were developed in the last years. Due to its influence on health, plants and construction relative humidity is another parameter of great importance. On the basis of 6 networks of measured (and homogenized with respect to the monthly means) relative humidity data, which cover different climatic areas in Austria, a synthetic data set for testing and validating homogenisation methods was built. Each network consists of 4 to 6 station time series with a minimum length of 5 years. The so-called surrogate networks resemble the statistical properties (e.g. distribution of parameter, auto- and cross correlation within the network) of the measured time series, but are extended to 100 year long time series, which are in a first step assumed to be homogeneous. For creating the best possible surrogate dataset of relative humidity detailed statistical information on potential inhomogeneities is decisive. Information on the potential breaks was taken from parallel measurements available for some Austrian locations, mostly representing changes in instrumentation and/or station relocation. Beside changes in the distribution of the parameter the analyses includes an estimation of changes in the number of missing data, global and local biases, both on a seasonal and annual basis. An additional break is to be expected in the Austrian time series due to a change in observation time in 1970/1971. Since this change occurred simultaneously at all Austrian climate stations, standard homogenisation methods, which rely on a comparison with reference stations, are not able to detect or correct this shift. Therefore an independent correction method for this type of break, to be applied before homogenisation was developed. This type of change point was not included in the surrogate network

Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Christiansen, Daniel E.; Haj, Adel E.; Risley, John C.

2017-10-24

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System

Time series ARIMA models for daily price of palm oil

NASA Astrophysics Data System (ADS)

Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

2015-02-01

Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

Relating precipitation to fronts at a sub-daily basis

NASA Astrophysics Data System (ADS)

Hénin, Riccardo; Ramos, Alexandre M.; Liberato, Margarida L. R.; Gouveia, Célia

2017-04-01

.M. Trigo and M.L.R. Liberato (2014) A ranking of high-resolution daily precipitation extreme events for the Iberian Peninsula, Atmospheric Science Letters 15, 328 - 334. doi: 10.1002/asl2.507. Shemm S., I. Rudeva and I. Simmonds (2014) Extratropical fronts in the lower troposphere - global perspectives obtained from two automated methods. Quarterly Journal of the Royal Meteorological Society, 141: 1686-1698, doi: 10.1002/qj.2471. ACKNOWLEDGEMENTS This work is supported by FCT - project UID/GEO/50019/2013 - Instituto Dom Luiz. Fundação para a Ciência e a Tecnologia, Portugal (FCT) is also providing for R. Hénin doctoral grant (PD/BD/114479/2016) and A.M. Ramos postdoctoral grant (FCT/DFRH/SFRH/BPD/84328/2012).

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2018-02-01

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

Predictive performance of rainfall thresholds for shallow landslide triggering in Switzerland from daily gridded precipitation data

NASA Astrophysics Data System (ADS)

Leonarduzzi, E.; Molnar, P.; McArdell, B. W.

2017-12-01

In Switzerland floods are responsible for most of the damage caused by rainfall-triggered natural hazards (89%), followed by landslides (6%, almost 600 M USD) as reported in Hilker et al. (2009) for the period 1972-2007. A high-resolution gridded daily precipitation dataset is combined with a landslide inventory containing over 2000 events in the period 1972-2012 to analyze rainfall thresholds that lead to landsliding in Switzerland. First triggering rainfall and landslides are co-located obtaining the distributions of triggering and non-triggering rainfall event properties at the scale of the precipitation data (2*2 km2) and considering 1 day as the interarrival time to separate events. Then rainfall thresholds are obtained by maximizing true positives (accurate predictions) while minimizing false negatives (false alarms), using the True Skill Statistic. The best predictive performance is obtained by the intensity-duration ID threshold curve, followed by peak daily intensity (Imax) and mean event intensity (Imean). Event duration by itself has very low predictive power. In addition to country-wide thresholds, local ones are also defined by regionalization based on surface erodibility and local long-term climate (mean daily precipitation). Different Imax thresholds are determined for each of the regions separately. It is found that wetter local climate and lower erodibility lead to significantly higher rainfall thresholds required to trigger landslides. However, the improvement in model performance due to regionalization is marginal and much lower than what can be achieved by having a high quality landslide database. In order to validate the performance of the Imax rainfall threshold model, reference cases will be presented in which the landslide locations and timing are randomized and the landslide sample size is reduced. Jack-knife and cross-validation experiments demonstrate that the model is robust. The results highlight the potential of using rainfall I

The impacts of precipitation amount simulation on hydrological modeling in Nordic watersheds

NASA Astrophysics Data System (ADS)

Li, Zhi; Brissette, Fancois; Chen, Jie

2013-04-01

Stochastic modeling of daily precipitation is very important for hydrological modeling, especially when no observed data are available. Precipitation is usually modeled by two component model: occurrence generation and amount simulation. For occurrence simulation, the most common method is the first-order two-state Markov chain due to its simplification and good performance. However, various probability distributions have been reported to simulate precipitation amount, and spatiotemporal differences exist in the applicability of different distribution models. Therefore, assessing the applicability of different distribution models is necessary in order to provide more accurate precipitation information. Six precipitation probability distributions (exponential, Gamma, Weibull, skewed normal, mixed exponential, and hybrid exponential/Pareto distributions) are directly and indirectly evaluated on their ability to reproduce the original observed time series of precipitation amount. Data from 24 weather stations and two watersheds (Chute-du-Diable and Yamaska watersheds) in the province of Quebec (Canada) are used for this assessment. Various indices or statistics, such as the mean, variance, frequency distribution and extreme values are used to quantify the performance in simulating the precipitation and discharge. Performance in reproducing key statistics of the precipitation time series is well correlated to the number of parameters of the distribution function, and the three-parameter precipitation models outperform the other models, with the mixed exponential distribution being the best at simulating daily precipitation. The advantage of using more complex precipitation distributions is not as clear-cut when the simulated time series are used to drive a hydrological model. While the advantage of using functions with more parameters is not nearly as obvious, the mixed exponential distribution appears nonetheless as the best candidate for hydrological modeling. The

Scaling behaviors of precipitation over China

NASA Astrophysics Data System (ADS)

Jiang, Lei; Li, Nana; Zhao, Xia

2017-04-01

Scaling behaviors in the precipitation time series derived from 1951 to 2009 over China are investigated by detrended fluctuation analysis (DFA) method. The results show that there exists long-term memory for the precipitation time series in some stations, where the values of the scaling exponent α are less than 0.62, implying weak persistence characteristics. The values of scaling exponent in other stations indicate random behaviors. In addition, the scaling exponent α in precipitation records varies from station to station over China. A numerical test is made to verify the significance in DFA exponents by shuffling the data records many times. We think it is significant when the values of scaling exponent before shuffled precipitation records are larger than the interval threshold for 95 % confidence level after shuffling precipitation records many times. By comparison, the daily precipitation records exhibit weak positively long-range correlation in a power law fashion mainly at the stations taking on zonal distributions in south China, upper and middle reaches of the Yellow River, northern part of northeast China. This may be related to the subtropical high. Furthermore, the values of scaling exponent which cannot pass the significance test do not show a clear distribution pattern. It seems that the stations are mainly distributed in coastal areas, southwest China, and southern part of north China. In fact, many complicated factors may affect the scaling behaviors of precipitation such as the system of the east and south Asian monsoon, the interaction between sea and land, and the big landform of the Tibetan Plateau. These results may provide a better prerequisite to long-term predictor of precipitation time series for different regions over China.

Changing patterns of daily precipitation totals at the Coweeta Hydrologic Laboratory, North Carolina, USA

Treesearch

T. P. Burt; C. Ford Miniat; S. H. Laseter; W. T. Swank

2017-01-01

A pattern of increasing frequency and intensity of heavy rainfall over land has been documented for several temperate regions and is associated with climate change. This study examines the changing patterns of daily precipitation at the Coweeta Hydrologic Laboratory, North Carolina, USA, since 1937 for four rain gauges across a range of elevations. We analyse...

Daily Streamflow Predictions in an Ungauged Watershed in Northern California Using the Precipitation-Runoff Modeling System (PRMS): Calibration Challenges when nearby Gauged Watersheds are Hydrologically Dissimilar

NASA Astrophysics Data System (ADS)

Dhakal, A. S.; Adera, S.

2017-12-01

Accurate daily streamflow prediction in ungauged watersheds with sparse information is challenging. The ability of a hydrologic model calibrated using nearby gauged watersheds to predict streamflow accurately depends on hydrologic similarities between the gauged and ungauged watersheds. This study examines daily streamflow predictions using the Precipitation-Runoff Modeling System (PRMS) for the largely ungauged San Antonio Creek watershed, a 96 km2 sub-watershed of the Alameda Creek watershed in Northern California. The process-based PRMS model is being used to improve the accuracy of recent San Antonio Creek streamflow predictions generated by two empirical methods. Although San Antonio Creek watershed is largely ungauged, daily streamflow data exists for hydrologic years (HY) 1913 - 1930. PRMS was calibrated for HY 1913 - 1930 using streamflow data, modern-day land use and PRISM precipitation distribution, and gauged precipitation and temperature data from a nearby watershed. The PRMS model was then used to generate daily streamflows for HY 1996-2013, during which the watershed was ungauged, and hydrologic responses were compared to two nearby gauged sub-watersheds of Alameda Creek. Finally, the PRMS-predicted daily flows between HY 1996-2013 were compared to the two empirically-predicted streamflow time series: (1) the reservoir mass balance method and (2) correlation of historical streamflows from 80 - 100 years ago between San Antonio Creek and a nearby sub-watershed located in Alameda Creek. While the mass balance approach using reservoir storage and transfers is helpful for estimating inflows to the reservoir, large discrepancies in daily streamflow estimation can arise. Similarly, correlation-based predicted daily flows which rely on a relationship from flows collected 80-100 years ago may not represent current watershed hydrologic conditions. This study aims to develop a method of streamflow prediction in the San Antonio Creek watershed by examining PRMS

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

NASA Astrophysics Data System (ADS)

Lussana, Cristian; Saloranta, Tuomo; Skaugen, Thomas; Magnusson, Jan; Tveito, Ole Einar; Andersen, Jess

2018-02-01

The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2

Daily time series evapotranspiration maps for Oklahoma and Texas panhandle

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is an important process in ecosystems’ water budget and closely linked to its productivity. Therefore, regional scale daily time series ET maps developed at high and medium resolutions have large utility in studying the carbon-energy-water nexus and managing water resources. ...

Trends in flash flood events versus convective precipitation in the Mediterranean region: The case of Catalonia

NASA Astrophysics Data System (ADS)

Llasat, Maria Carmen; Marcos, Raul; Turco, Marco; Gilabert, Joan; Llasat-Botija, Montserrat

2016-10-01

The aim of this paper is to analyse the potential relationship between flash flood events and convective precipitation in Catalonia, as well as any related trends. The paper starts with an overview of flash floods and their trends in the Mediterranean region, along with their associated factors, followed by the definition of, identification of, and trends in convective precipitation. After this introduction the paper focuses on the north-eastern Iberian Peninsula, for which there is a long-term precipitation series (since 1928) of 1-min precipitation from the Fabra Observatory, as well as a shorter (1996-2011) but more extensive precipitation series (43 rain gauges) of 5-min precipitation. Both series have been used to characterise the degree of convective contribution to rainfall, introducing the β parameter as the ratio between convective precipitation versus total precipitation in any period. Information about flood events was obtained from the INUNGAMA database (a flood database created by the GAMA team), with the aim of finding any potential links to convective precipitation. These flood data were gathered using information on damage where flood is treated as a multifactorial risk, and where any trend or anomaly might have been caused by one or more factors affecting hazard, vulnerability or exposure. Trend analysis has shown an increase in flash flood events. The fact that no trends were detected in terms of extreme values of precipitation on a daily scale, nor on the associated ETCCDI (Expert Team on Climate Change Detection and Indices) extreme index, could point to an increase in vulnerability, an increase in exposure, or changes in land use. However, the summer increase in convective precipitation was concentrated in less torrential events, which could partially explain this positive trend in flash flood events. The β parameter has been also used to characterise the type of flood event according to the features of the precipitation. The highest values

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2015-04-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics

NASA Astrophysics Data System (ADS)

Westerberg, I.; Walther, A.; Guerrero, J.-L.; Coello, Z.; Halldin, S.; Xu, C.-Y.; Chen, D.; Lundin, L.-C.

2010-08-01

An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913-2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap-filling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4-5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitation-generating mechanisms and the need for an improved monitoring network.

Assessment of Multiple Daily Precipitation Statistics in ERA-Interim Driven Med-CORDEX and EURO-CORDEX Experiments Against High Resolution Observations

NASA Astrophysics Data System (ADS)

Coppola, E.; Fantini, A.; Raffaele, F.; Torma, C. Z.; Bacer, S.; Giorgi, F.; Ahrens, B.; Dubois, C.; Sanchez, E.; Verdecchia, M.

2017-12-01

We assess the statistics of different daily precipitation indices in ensembles of Med-CORDEX and EUROCORDEX experiments at high resolution (grid spacing of ˜0.11° , or RCM11) and medium resolution (grid spacing of ˜0.44° , or RCM44) with regional climate models (RCMs) driven by the ERA-Interim reanalysis of observations for the period 1989-2008. The assessment is carried out by comparison with a set of high resolution observation datasets for 9 European subregions. The statistics analyzed include quantitative metrics for mean precipitation, daily precipitation Probability Density Functions (PDFs), daily precipitation intensity, frequency, 95th percentile and 95th percentile of dry spell length. We assess both an ensemble including all Med-CORDEX and EURO-CORDEX models and one including the Med-CORDEX models alone. For the All Models ensembles, the RCM11 one shows a remarkable performance in reproducing the spatial patterns and seasonal cycle of mean precipitation over all regions, with a consistent and marked improvement compared to the RCM44 ensemble and the ERA-Interim reanalysis. A good consistency with observations by the RCM11 ensemble (and a substantial improvement compared to RCM44 and ERA-Interim) is found also for the daily precipitation PDFs, mean intensity and, to a lesser extent, the 95th percentile. In fact, for some regions the RCM11 ensemble overestimates the occurrence of very high intensity events while for one region the models underestimate the occurrence of the largest extremes. The RCM11 ensemble still shows a general tendency to underestimate the dry day frequency and 95th percentile of dry spell length over wetter regions, with only a marginal improvement compared to the lower resolution models. This indicates that the problem of the excessive production of low precipitation events found in many climate models persists also at relatively high resolutions, at least in wet climate regimes. Concerning the Med-CORDEX model ensembles we find

Pollen exposure and hospitalization due to asthma exacerbations: daily time series in a European city.

PubMed

Osborne, Nicholas J; Alcock, Ian; Wheeler, Benedict W; Hajat, Shakoor; Sarran, Christophe; Clewlow, Yolanda; McInnes, Rachel N; Hemming, Deborah; White, Mathew; Vardoulakis, Sotiris; Fleming, Lora E

2017-10-01

Exposure to pollen can contribute to increased hospital admissions for asthma exacerbation. This study applied an ecological time series analysis to examine associations between atmospheric concentrations of different pollen types and the risk of hospitalization for asthma in London from 2005 to 2011. The analysis examined short-term associations between daily pollen counts and hospital admissions in the presence of seasonal and long-term patterns, and allowed for time lags between exposure and admission. Models were adjusted for temperature, precipitation, humidity, day of week, and air pollutants. Analyses revealed an association between daily counts (continuous) of grass pollen and adult hospital admissions for asthma in London, with a 4-5-day lag. When grass pollen concentrations were categorized into Met Office pollen 'alert' levels, 'very high' days (vs. 'low') were associated with increased admissions 2-5 days later, peaking at an incidence rate ratio of 1.46 (95%, CI 1.20-1.78) at 3 days. Increased admissions were also associated with 'high' versus 'low' pollen days at a 3-day lag. Results from tree pollen models were inconclusive and likely to have been affected by the shorter pollen seasons and consequent limited number of observation days with higher tree pollen concentrations. Future reductions in asthma hospitalizations may be achieved by better understanding of environmental risks, informing improved alert systems and supporting patients to take preventive measures.

Pollen exposure and hospitalization due to asthma exacerbations: daily time series in a European city

NASA Astrophysics Data System (ADS)

Osborne, Nicholas J.; Alcock, Ian; Wheeler, Benedict W.; Hajat, Shakoor; Sarran, Christophe; Clewlow, Yolanda; McInnes, Rachel N.; Hemming, Deborah; White, Mathew; Vardoulakis, Sotiris; Fleming, Lora E.

2017-10-01

Exposure to pollen can contribute to increased hospital admissions for asthma exacerbation. This study applied an ecological time series analysis to examine associations between atmospheric concentrations of different pollen types and the risk of hospitalization for asthma in London from 2005 to 2011. The analysis examined short-term associations between daily pollen counts and hospital admissions in the presence of seasonal and long-term patterns, and allowed for time lags between exposure and admission. Models were adjusted for temperature, precipitation, humidity, day of week, and air pollutants. Analyses revealed an association between daily counts (continuous) of grass pollen and adult hospital admissions for asthma in London, with a 4-5-day lag. When grass pollen concentrations were categorized into Met Office pollen `alert' levels, `very high' days (vs. `low') were associated with increased admissions 2-5 days later, peaking at an incidence rate ratio of 1.46 (95%, CI 1.20-1.78) at 3 days. Increased admissions were also associated with `high' versus `low' pollen days at a 3-day lag. Results from tree pollen models were inconclusive and likely to have been affected by the shorter pollen seasons and consequent limited number of observation days with higher tree pollen concentrations. Future reductions in asthma hospitalizations may be achieved by better understanding of environmental risks, informing improved alert systems and supporting patients to take preventive measures.

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series

Temporal asymmetry in precipitation time series and its influence on flow simulations in combined sewer systems

NASA Astrophysics Data System (ADS)

Müller, Thomas; Schütze, Manfred; Bárdossy, András

2017-09-01

A property of natural processes is temporal irreversibility. However, this property cannot be reflected by most statistics used to describe precipitation time series and, consequently, is not considered in most precipitation models. In this paper, a new statistic, the asymmetry measure, is introduced and applied to precipitation enabling to detect and quantify irreversibility. It is used to analyze two different data sets of Singapore and Germany. The data of both locations show a significant asymmetry for high temporal resolutions. The asymmetry is more pronounced for Singapore where the climate is dominated by convective precipitation events. The impact of irreversibility on applications is analyzed on two different hydrological sewer system models. The results show that the effect of the irreversibility can lead to biases in combined sewer overflow statistics. This bias is in the same order as the effect that can be achieved by real time control of sewer systems. Consequently, wrong conclusion can be drawn if synthetic time series are used for sewer systems if asymmetry is present, but not considered in precipitation modeling.

Empirical meaning of DTM multifractal parameters in the precipitation context

NASA Astrophysics Data System (ADS)

Portilla Farfan, Freddy; Valencia, Jose Luis; Villeta, Maria; Tarquis, Ana M.; Saa-Requejo, Antonio

2015-04-01

The main objective of this research is to interpret the multifractal parameters in the case of precipitation series from an empirical approach. In order to do so nineteen precipitation series were generated with a daily precipitation simulator derived from year and month estimations and considering the classical statistics, used commonly in hydrology studies, from actual data of four Spanish rain gauges located in a gradient from NW to SE. For all generated series the multifractal parameters were estimated following the technique DTM (Double Trace Moments) developed by Lavalle et al. (1993) and the variations produced considered. The results show the following conclusions: 1. The intermittency, C1, increases when precipitation is concentrating for fewer days, making it more variable, or when increasing its concentration on maximum monthly precipitation days, while it is not affected due to the modification in the variability in the number of days rained. 2. Multifractility, α, increases with the number of rainy days and the variability of the precipitation, yearly as well as monthly, as well as with the concentration of precipitation on the maximum monthly precipitation day. 3. The maximum probable singularity, γs, increases with the concentration of rain on the day of the maximum monthly precipitation and the variability in yearly and monthly level. 4. The non-conservative degree, H, depends on the number of rainy days that appear on the series and secondly on the general variability of the rain. References Lavallée D., S. Lovejoy, D. Schertzer and P. Ladoy, 1993. Nonlinear variability and landscape topography: analysis and simulation. In: Fractals in Geography (N. Lam and L. De Cola, Eds.) Prentice Hall, Englewood Cliffs, 158-192.

Space-time characteristics and statistical predictability of extreme daily precipitation events in the Ohio River Basin

NASA Astrophysics Data System (ADS)

Farnham, D. J.; Doss-Gollin, J.; Lall, U.

2016-12-01

In this study we identify the atmospheric conditions that precede and accompany regional extreme precipitation events with the potential to cause flooding. We begin by identifying a coherent space-time structure in the record of extreme precipitation within the Ohio River Basin through both a Hidden Markov Model and a composite analysis. The transition probabilities associated with the Hidden Markov Model illustrate a tendency for west to east migration of extreme precipitation events (> 99th percentile) at individual stations within the Ohio River Basin. We compute a record of regional extreme precipitation days by requiring that > p% of the basin's stations simultaneously experience extreme precipitation days. A composite analysis of low-level geopotential heights and column integrated precipitable water content for all non-summer seasons confirms a west to east migration and intensification of 1) a low (high) pressure center to the west (east) of the basin, and 2) enhanced precipitable water vapor content that stretches from the Gulf of Mexico to the Northeast US region in the days leading up to regional extreme precipitation days. We define a daily dipole index to summarize the strength of the paired cylonic and aniticyclonic systems to the west and east of the basin and analyze its temporal characteristics and its relationship to the regional extreme precipitation events. Lastly, we investigate and discuss the subseasonal predictability of individual extreme precipitation events and the seasonal predictability of active and inactive seasons, where the activity level is defined by the expected frequency of regional extreme precipitation events.

Time Series in Education: The Analysis of Daily Attendance in Two High Schools

ERIC Educational Resources Information Center

Koopmans, Matthijs

2011-01-01

This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…

Trends in extremes of temperature, dew point, and precipitation from long instrumental series from central Europe

NASA Astrophysics Data System (ADS)

Kürbis, K.; Mudelsee, M.; Tetzlaff, G.; Brázdil, R.

2009-09-01

For the analysis of trends in weather extremes, we introduce a diagnostic index variable, the exceedance product, which combines intensity and frequency of extremes. We separate trends in higher moments from trends in mean or standard deviation and use bootstrap resampling to evaluate statistical significances. The application of the concept of the exceedance product to daily meteorological time series from Potsdam (1893 to 2005) and Prague-Klementinum (1775 to 2004) reveals that extremely cold winters occurred only until the mid-20th century, whereas warm winters show upward trends. These changes were significant in higher moments of the temperature distribution. In contrast, trends in summer temperature extremes (e.g., the 2003 European heatwave) can be explained by linear changes in mean or standard deviation. While precipitation at Potsdam does not show pronounced trends, dew point does exhibit a change from maximum extremes during the 1960s to minimum extremes during the 1970s.

CHANGES IN ANTHROPOGENIC INPERVIOUS SURFACES, PRECIPITATION AND DAILY STREAMFLOW DISCHARGE: A HISTORICAL PERSPECTIVE IN A MID-ATLANTIC SUBWATERSHED

EPA Science Inventory

Aerial photography provides a historical vehicle for determining long term urban landscape change and, with concurrent daily streamflow and precipitation records, allows the historical relationship of impervious surfaces and streamflow to be explored. Impervious surface area in ...

Evaluation of Satellite and Model Precipitation Products Over Turkey

NASA Astrophysics Data System (ADS)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14

Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales

NASA Astrophysics Data System (ADS)

Katiraie-Boroujerdy, Pari-Sima; Akbari Asanjan, Ata; Hsu, Kuo-lin; Sorooshian, Soroosh

2017-09-01

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648-0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349-0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7

Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

USGS Publications Warehouse

Christiansen, Daniel E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota

Generating daily weather data for ecosystem modelling in the Congo River Basin

NASA Astrophysics Data System (ADS)

Petritsch, Richard; Pietsch, Stephan A.

2010-05-01

Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range

Multisite stochastic simulation of daily precipitation from copula modeling with a gamma marginal distribution

NASA Astrophysics Data System (ADS)

Lee, Taesam

2018-05-01

Multisite stochastic simulations of daily precipitation have been widely employed in hydrologic analyses for climate change assessment and agricultural model inputs. Recently, a copula model with a gamma marginal distribution has become one of the common approaches for simulating precipitation at multiple sites. Here, we tested the correlation structure of the copula modeling. The results indicate that there is a significant underestimation of the correlation in the simulated data compared to the observed data. Therefore, we proposed an indirect method for estimating the cross-correlations when simulating precipitation at multiple stations. We used the full relationship between the correlation of the observed data and the normally transformed data. Although this indirect method offers certain improvements in preserving the cross-correlations between sites in the original domain, the method was not reliable in application. Therefore, we further improved a simulation-based method (SBM) that was developed to model the multisite precipitation occurrence. The SBM preserved well the cross-correlations of the original domain. The SBM method provides around 0.2 better cross-correlation than the direct method and around 0.1 degree better than the indirect method. The three models were applied to the stations in the Nakdong River basin, and the SBM was the best alternative for reproducing the historical cross-correlation. The direct method significantly underestimates the correlations among the observed data, and the indirect method appeared to be unreliable.

Spectral analysis of luni-solar signal in daily meteorological data at Prague-Klementinum 1775 to 2011

NASA Astrophysics Data System (ADS)

Hejkrlík, L.

2012-04-01

Linkages between lunar synodic cycle and select types of meteorological parameters have been suggested in previous studies. In important papers it was demonstrated that extreme precipitation events occur more frequently on the third to fifth day after syzygies. The effect is sometimes called Bowen's signal and similar lunar or semi-lunar modulation was later found also in ozone concentrations, sunshine, thunderstorm frequencies and in global temperatures observed by polar orbiting satellites. In our earlier papers we tried to analyse the possibility that the effect is transient in relation to solar activity. We confirmed the long-term quasi-periodical nearly-parallel changes in expression of lunar signal in 14 century-long daily precipitation series across Europe. The prevailing periods seemed to be similar to 22-year Hale solar magnetic cycle but there was no clear evidence and other similar celestial cycles could not be excluded. A unique set of uninterrupted daily precipitation data from Prague-Klementinum since 1804, published by the Czech Hydrometeorological Institute, was available. In this study we also made use of a related dataset of daily mean cloudiness that dates back as far as 1775. The cloud cover has been observed in a subjective way but we think its reliability is relatively good. In the case of precipitation we also examined the series of days with daily amount exceeding 10 mm. The data were divided into epochs of synodic months and superposed for 5 or 11 years with a step of one year. We get six sets of mean 29-day synodic signals containing 198-204 records for precipitation and 227-233 records for cloudiness. The temporal occurrence of lunar variation was defined as correlation coefficient ψ between semi-lunar cosine function (period 4π/29.53), emulating the Bowen's signal, and the mean signals. The coefficients ψ drawn against time created quasi-periodical charts ranging over more than two centuries. The impression of the ever

Daily temperature and precipitation extremes in the Baltic Sea region derived from the BaltAn65+ reanalysis

NASA Astrophysics Data System (ADS)

Toll, Velle; Post, Piia

2018-04-01

Daily 2-m temperature and precipitation extremes in the Baltic Sea region for the time period of 1965-2005 is studied based on data from the BaltAn65 + high resolution atmospheric reanalysis. Moreover, the ability of regional reanalysis to capture extremes is analysed by comparing the reanalysis data to gridded observations. The shortcomings in the simulation of the minimum temperatures over the northern part of the region and in the simulation of the extreme precipitation over the Scandinavian mountains in the BaltAn65+ reanalysis data are detected and analysed. Temporal trends in the temperature and precipitation extremes in the Baltic Sea region, with the largest increases in temperature and precipitation in winter, are detected based on both gridded observations and the BaltAn65+ reanalysis data. However, the reanalysis is not able to capture all of the regional trends in the extremes in the observations due to the shortcomings in the simulation of the extremes.

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series

CHANGES IN ANTHROPOGENIC IMPERVIOUS SURFACES, PRECIPITATION AND DAILY STREAMFLOW DISCHARGE: A HISTORICAL PERSPECTIVE IN A MID-ATLANTIC SUB-WATERSHED

EPA Science Inventory

Aerial photography provides a historical vehicle for determining long term urban landscape change and, with concurrent daily streamflow and precipitation records, allows the historical relationship of impervious surfaces and streamflow to be explored. Impervious surfac...

Spatiotemporal Patterns of Precipitation-Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series

NASA Astrophysics Data System (ADS)

Cohen-Waeber, J.; Bürgmann, R.; Chaussard, E.; Giannico, C.; Ferretti, A.

2018-02-01

Long-term landslide deformation is disruptive and costly in urbanized environments. We rely on TerraSAR-X satellite images (2009-2014) and an improved data processing algorithm (SqueeSAR™) to produce an exceptionally dense Interferometric Synthetic Aperture Radar ground deformation time series for the San Francisco East Bay Hills. Independent and principal component analyses of the time series reveal four distinct spatial and temporal surface deformation patterns in the area around Blakemont landslide, which we relate to different geomechanical processes. Two components of time-dependent landslide deformation isolate continuous motion and motion driven by precipitation-modulated pore pressure changes controlled by annual seasonal cycles and multiyear drought conditions. Two components capturing more widespread seasonal deformation separate precipitation-modulated soil swelling from annual cycles that may be related to groundwater level changes and thermal expansion of buildings. High-resolution characterization of landslide response to precipitation is a first step toward improved hazard forecasting.

Multi-site precipitation downscaling using a stochastic weather generator

NASA Astrophysics Data System (ADS)

Chen, Jie; Chen, Hua; Guo, Shenglian

2018-03-01

Statistical downscaling is an efficient way to solve the spatiotemporal mismatch between climate model outputs and the data requirements of hydrological models. However, the most commonly-used downscaling method only produces climate change scenarios for a specific site or watershed average, which is unable to drive distributed hydrological models to study the spatial variability of climate change impacts. By coupling a single-site downscaling method and a multi-site weather generator, this study proposes a multi-site downscaling approach for hydrological climate change impact studies. Multi-site downscaling is done in two stages. The first stage involves spatially downscaling climate model-simulated monthly precipitation from grid scale to a specific site using a quantile mapping method, and the second stage involves the temporal disaggregating of monthly precipitation to daily values by adjusting the parameters of a multi-site weather generator. The inter-station correlation is specifically considered using a distribution-free approach along with an iterative algorithm. The performance of the downscaling approach is illustrated using a 10-station watershed as an example. The precipitation time series derived from the National Centers for Environment Prediction (NCEP) reanalysis dataset is used as the climate model simulation. The precipitation time series of each station is divided into 30 odd years for calibration and 29 even years for validation. Several metrics, including the frequencies of wet and dry spells and statistics of the daily, monthly and annual precipitation are used as criteria to evaluate the multi-site downscaling approach. The results show that the frequencies of wet and dry spells are well reproduced for all stations. In addition, the multi-site downscaling approach performs well with respect to reproducing precipitation statistics, especially at monthly and annual timescales. The remaining biases mainly result from the non-stationarity of

Deriving Daily Time Series Evapotranspiration, Evaporation and Transpiration Maps With Landsat Data

NASA Astrophysics Data System (ADS)

Paul, G.; Gowda, P. H.; Marek, T.; Xiao, X.; Basara, J. B.

2014-12-01

Mapping high resolution evapotranspiration (ET) over large region at daily time step is complex and computationally intensive. Utility of high resolution daily ET maps are large ranging from crop water management to watershed management. The aim of this work is to generate daily time series (10 years) ET and its components vegetation transpiration (T) and soil water evaporation (E) maps using Landsat 5 satellite data for Southern Great Plains forage-rangeland-winter wheat production system in Oklahoma (OK). Framework for generating these products included the two source energy balance (TSEB) algorithm and other important features were: (a) atmospheric correction algorithm; (b) spatially interpolated weather inputs; (c) functions for varying Priestley-Taylor coefficient; and (d) ET, E and T extrapolating algorithm utilizing reference ET. An extensive network of 140 weather stations managed by Oklahoma Mesonet was utilized to generate spatially interpolated inputs of air temperature, relative humidity, wind speed, solar radiation, pressure, and reference ET. Validation of the ET maps were done against eddy covariance data from two grassland sites at El Reno, OK suggested good performance (Table 1). Figure 1 illustrates a daily ET map for a very small subset of 18thJuly 2006 ET map, where difference in ET among different land uses such as the irrigated cropland, vegetation along drainage, and grassland is very distinct. Results indicated that the proposed ET mapping framework is suitable for deriving high resolution time series daily ET maps at regional scale with Landsat Thematic Mapper data. . Table 1: Daily actual ET performance statistics for two grassland locations at El Reno OK for year 2005 . Management Type Mean (obs) (mm d-1) Mean (est) (mm d-1) MBE (mm d-1) % MBE (%) RMSE (mm d-1) RMSE (%) MAE (mm d-1) MAPD (%) NSE R2 Control 2.2 1.8 -0.43 -19.4 0.87 38.9 0.65 29.5 0.71 0.79 Burnt 2.0 1.8 -0.15 -7.7 0.80 39.8 0.62 30.7 0.73 0.77

Arctic daily temperature and precipitation extremes: Observed and simulated physical behavior

NASA Astrophysics Data System (ADS)

Glisan, Justin Michael

Simulations using a six-member ensemble of Pan-Arctic WRF (PAW) were produced on two Arctic domains with 50-km resolution to analyze precipitation and temperature extremes for various periods. The first study used a domain developed for the Regional Arctic Climate Model (RACM). Initial simulations revealed deep atmospheric circulation biases over the northern Pacific Ocean, manifested in pressure, geopotential height, and temperature fields. Possible remedies to correct these large biases, such as modifying the physical domain or using different initial/boundary conditions, were unsuccessful. Spectral (interior) nudging was introduced as a way of constraining the model to be more consistent with observed behavior. However, such control over numerical model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events, since the nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes---what is the minimum spectral nudging needed to correct biases while not limiting the simulation of extreme events? To determine this, we use varying degrees of spectral nudging, using WRF's standard nudging as a reference point during January and July 2007. Results suggest that there is a marked lack of sensitivity to varying degrees of nudging. Moreover, given that nudging is an artificial forcing applied in the model, an important outcome of this work is that nudging strength apparently can be considerably smaller than WRF's standard strength and still produce reliable simulations. In the remaining studies, we used the same PAW setup to analyze daily precipitation extremes simulated over a 19-year period on the CORDEX Arctic domain for winter and summer. We defined these seasons as the three-month period leading up to and including the climatological sea ice maximum and minimum, respectively. Analysis focused on four North American regions defined using

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3

Trend analysis of air temperature and precipitation time series over Greece: 1955-2010

NASA Astrophysics Data System (ADS)

Marougianni, G.; Melas, D.; Kioutsioukis, I.; Feidas, H.; Zanis, P.; Anandranistakis, E.

2012-04-01

In this study, a database of air temperature and precipitation time series from the network of Hellenic National Meteorological Service has been developed in the framework of the project GEOCLIMA, co-financed by the European Union and Greek national funds through the Operational Program "Competitiveness and Entrepreneurship" of the Research Funding Program COOPERATION 2009. Initially, a quality test was applied to the raw data and then missing observations have been imputed with a regularized, spatial-temporal expectation - maximization algorithm to complete the climatic record. Next, a quantile - matching algorithm was applied in order to verify the homogeneity of the data. The processed time series were used for the calculation of temporal annual and seasonal trends of air temperature and precipitation. Monthly maximum and minimum surface air temperature and precipitation means at all available stations in Greece were analyzed for temporal trends and spatial variation patterns for the longest common time period of homogenous data (1955 - 2010), applying the Mann-Kendall test. The majority of the examined stations showed a significant increase in the summer maximum and minimum temperatures; this could be possibly physically linked to the Etesian winds, because of the less frequent expansion of the low over the southeastern Mediterranean. Summer minimum temperatures have been increasing at a faster rate than that of summer maximum temperatures, reflecting an asymmetric change of extreme temperature distributions. Total annual precipitation has been significantly decreased at the stations located in western Greece, as well as in the southeast, while the remaining areas exhibit a non-significant negative trend. This reduction is very likely linked to the positive phase of the NAO that resulted in an increase in the frequency and persistence of anticyclones over the Mediterranean.

Variogram analysis of stable oxygen isotope composition of daily precipitation over the British Isles

NASA Astrophysics Data System (ADS)

Kohán, Balázs; Tyler, Jonathan; Jones, Matthew; Kern, Zoltán

2017-04-01

Water stable isotopes are important natural tracers in the hydrological cycle on global, regional and local scales. Daily precipitation water samples were collected from 70 sites over the British Isles on the 23rd, 24th, and 25th January, 2012 [1]. Samples were collected as part of a pilot study for the British Isotopes in Rainfall Project, a community engagement initiative, in collaboration with volunteer weather observers and the UK Met Office. Spatial correlation structure of daily precipitation stable oxygen isotope composition (δ18OP) has been explored by variogram analysis [2]. Since the variograms from the raw data suggested a pronounced trend, owing to the spatial trend discussed in the original study [1], a second order polynomial trend was removed from the raw δ18OP data and variograms were calculated on the residuals. Directional experimental semivariograms were calculated (steps: 10°, tolerance: 30°) and aggregated into variogram surface plots to explore the spatial dependence structure of daily δ18OP. Each daily data set produced distinct variogram plots. -A well expressed anisotropic structure can be seen for Jan 23. The lowest and highest variance was observed in the SW-NE and NNE-SSW direction, respectively. Meteorological observations showed that the majority of the atmospheric flow was SW on this day, so the direction of low variance seems to reflect this flow direction, while the maximum variance might reflect the moisture variance near the elongation of the frontal system. -A less characteristic but still expressed anisotropic structure was found for Jan 24 when a warm front passed the British Isles perpendicular to the east coast, leading to a characteristic east-west δ18OP gradient suggestive of progressive rainout. The low variance central zone has a 100 km radius which might correspond well to the width of the warm front zone. Although, the axis of minimum variance was similarly SW-NE, the zone of maximum variance was broader and

Analysis of satellite precipitation over East Africa during last decades

NASA Astrophysics Data System (ADS)

Cattani, Elsa; Wenhaji Ndomeni, Claudine; Merino, Andrés; Levizzani, Vincenzo

2016-04-01

Daily accumulated precipitation time series from satellite retrieval algorithms (e.g., ARC2 and TAMSAT) are exploited to extract the spatial and temporal variability of East Africa (EA - 5°S-20°N, 28°E-52°E) precipitation during last decades (1983-2013). The Empirical Orthogonal Function (EOF) analysis is applied to precipitation time series to investigate the spatial and temporal variability in particular for October-November-December referred to as the short rain season. Moreover, the connection among EA's precipitation, sea surface temperature, and soil moisture is analyzed through the correlation with the dominant EOF modes of variability. Preliminary results concern the first two EOF's modes for the ARC2 data set. EOF1 is characterized by an inter-annual variability and a positive correlation between precipitation and El Niño, positive Indian Ocean Dipole mode, and soil moisture, while EOF2 shows a dipole structure of spatial variability associated with a longer scale temporal variability. This second dominant mode is mostly linked to sea surface temperature variations in the North Atlantic Ocean. Further analyses are carried out by computing the time series of the joint CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI, http://etccdi.pacificclimate.org/index.shtml), i.e. RX1day, RX5day, CDD, CDD, CWD, SDII, PRCPTOT, R10, R20. The purpose is to identify the occurrenes of extreme events (droughts and floods) and extract precipitation temporal variation by trend analysis (Mann-Kendall technique). Results for the ARC2 data set demonstrate the existence of a dipole spatial pattern in the linear trend of the time series of PRCPTOT (annual precipitation considering days with a rain rate > 1 mm) and SDII (average precipitation on wet days over a year). A negative trend is mainly present over West Ethiopia and Sudan, whereas a positive trend is exhibited over East Ethiopia and Somalia. CDD (maximum number of consecutive dry days) and

The 500-year temperature and precipitation fluctuations in the Czech Lands derived from documentary evidence and instrumental measurements

NASA Astrophysics Data System (ADS)

Dobrovolný, Petr; Brázdil, Rudolf; Kotyza, Oldřich; Valášek, Hubert

2010-05-01

Series of temperature and precipitation indices (in ordinal scale) based on interpretation of various sources of documentary evidence (e.g. narrative written reports, visual daily weather records, personal correspondence, special prints, official economic records, etc.) are used as predictors in the reconstruction of mean seasonal temperatures and seasonal precipitation totals for the Czech Lands from A.D. 1500. Long instrumental measurements from 1771 (temperatures) and 1805 (precipitation) are used as a target values to calibrate and verify documentary-based index series. Reconstruction is based on linear regression with variance and mean adjustments. Reconstructed series were compared with similar European documentary-based reconstructions as well as with reconstructions based on different natural proxies. Reconstructed series were analyzed with respect to trends on different time-scales and occurrence of extreme values. We discuss uncertainties typical for documentary evidence from historical archives. Besides the fact that reports on weather and climate in documentary archives cover all seasons, our reconstructions provide the best results for winter temperatures and summer precipitation. However, explained variance for these seasons is comparable to other existing reconstructions for Central Europe.

Precipitation extremes on multiple timescales - Bartlett-Lewis rectangular pulse model and intensity-duration-frequency curves

NASA Astrophysics Data System (ADS)

Ritschel, Christoph; Ulbrich, Uwe; Névir, Peter; Rust, Henning W.

2017-12-01

For several hydrological modelling tasks, precipitation time series with a high (i.e. sub-daily) resolution are indispensable. The data are, however, not always available, and thus model simulations are used to compensate. A canonical class of stochastic models for sub-daily precipitation are Poisson cluster processes, with the original Bartlett-Lewis (OBL) model as a prominent representative. The OBL model has been shown to well reproduce certain characteristics found in observations. Our focus is on intensity-duration-frequency (IDF) relationships, which are of particular interest in risk assessment. Based on a high-resolution precipitation time series (5 min) from Berlin-Dahlem, OBL model parameters are estimated and IDF curves are obtained on the one hand directly from the observations and on the other hand from OBL model simulations. Comparing the resulting IDF curves suggests that the OBL model is able to reproduce the main features of IDF statistics across several durations but cannot capture rare events (here an event with a return period larger than 1000 years on the hourly timescale). In this paper, IDF curves are estimated based on a parametric model for the duration dependence of the scale parameter in the generalized extreme value distribution; this allows us to obtain a consistent set of curves over all durations. We use the OBL model to investigate the validity of this approach based on simulated long time series.

Exploring the new long-term (150 years) precipitation dataset in Azores archipelago

NASA Astrophysics Data System (ADS)

Hernández, Armand; Trigo, Ricardo M.; Kutiel, Haim; Valente, Maria A.; Sigró, Javier

2015-04-01

Within the scope of the two major international projects of long-term reanalysis for the 20th century coordinated by NOAA (Compo et al. 2011) and ECMWF (Hersbach et al. 2013) the IDL Institute from the University of Lisbon has digitized a large number of long-term stations records from Portugal and former Portuguese Colonies (Stickler et al. 2014). Recently we have finished the digitization of all precipitation values from Ponta Delgada (capital of the Azores archipelago) obtaining an uninterrupted precipitation monthly time series since 1864 and additionally an almost complete corresponding daily precipitation series, with the exception of some years (1864/1872; 1878/1879; 1888/1905; 1931; 1936 and 1938) for which only monthly values are available. Here, we present an annually, seasonally and daily resolution study of the rainfall regime in Ponta Delgada for the last 150 years and the North Atlantic Oscillation (NAO) influence over this precipitation regime. The distribution of precipitation presents an evident seasonal pattern, with a strong difference between the 'rainy season' (November/March) and the 'dry season' (June/August) with very little rainfall. April/May and September/October correspond to the transitional seasons. The mean annual rainfall in Ponta Delgada is approximately 910 mm and is accumulated (on average) in about 120 rainy days. The precipitation regime in Azores archipelago reveals large inter-annual and intra-annual variability and both have increased considerably in the last decades. The entire studied period (1865-2012) shows an increase in the rainfall conditions between a drier earlier period (1865-1938) and a wetter recent period (1939-2012). At daily resolution, we have used an approach based on different characteristics of rain spells (consecutive days with rainfall accumulation) that has been proved to be satisfactory for the analysis of the different parameters related to the rainfall regime (Kutiel and Trigo, 2014). This approach

Towards a new daily in-situ precipitation data set supporting parameterization of wet-deposition of CTBT relevant radionuclides

NASA Astrophysics Data System (ADS)

Becker, A.; Ceranna, L.; Ross, O.; Schneider, U.; Meyer-Christoffer, A.; Ziese, M.; Lehner, K.; Rudolf, B.

2012-04-01

release events like the Fukushima accident that need to be classified as bogus by a properly working RN verification regime. For these kinds of events a higher temporal resolution of the precipitation data sets is needed. In course of the research project 'Global DAily Precipitation Analysis for the validation of medium-range CLImate Predictions (DAPACLIP) within the Framework Research Programme MiKlip (Mittelfristige Klimaprognose), funded by the German ministry for research (BMBF), a new quality controlled and globally gridded daily precipitation data set is built up, where GPCC will serve the land-surface compartment. The data set is primarily constructed to study decadal behaviour of the essential climate variable precipitation, but as a collateral benefit it will also serve RN verification regime needs. The Fukushima accident has also provided impetus to construct even hourly in-situ precipitation data sets as will be presented in the same session by Yatagai (2012). A comprehensive overview on available precipitation data sets based on in-situ (rain gauge), satellite measurements or the combination of both systems is available from the International Precipitation Working Group (IPWG) web pages (http://www.isac.cnr.it/~ipwg/data/datasets.html).

A new index quantifying the precipitation extremes

NASA Astrophysics Data System (ADS)

Busuioc, Aristita; Baciu, Madalina; Stoica, Cerasela

2015-04-01

Meteorological Administration in Romania. These types of records contain the rainfall intensity (mm/minute) over various intervals for which it remains constant. The maximum intensity for each continuous rain over the May-August interval has been calculated for each year. The corresponding time series over the 1951-2008 period have been analysed in terms of their long term trends and shifts in the mean; the results have been compared to those resulted from other rainfall indices based on daily and hourly data, computed over the same interval such as: total rainfall amount, maximum daily amount, contribution of total hourly amounts exceeding 10mm/day, contribution of daily amounts exceeding the 90th percentile, the 90th, 99th and 99.9th percentiles of 1-hour data . The results show that the proposed index exhibit a coherent and stronger climate signal (significant increase) for all analysed stations compared to the other indices associated to precipitation extremes, which show either no significant change or weaker signal. This finding shows that the proposed index is most appropriate to quantify the climate change signal of the precipitation extremes. We consider that this index is more naturally connected to the maximum intensity of a real rainfall event. The results presented is this study were funded by the Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) through the research project CLIMHYDEX, "Changes in climate extremes and associated impact in hydrological events in Romania", code PNII-ID-2011-2-0073 (http://climhydex.meteoromania.ro)

A new precipitation and drought climatology based on weather patterns.

PubMed

Richardson, Douglas; Fowler, Hayley J; Kilsby, Christopher G; Neal, Robert

2018-02-01

Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterize the broad-scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI-based drought months. The new weather-pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation-based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification-based analyses in the UK.

Relative importance of precipitation frequency and intensity in inter-annual variation of precipitation in Singapore during 1980-2013

NASA Astrophysics Data System (ADS)

Li, Xin; Babovic, Vladan

2017-04-01

Observed studies on inter-annual variation of precipitation provide insight into the response of precipitation to anthropogenic climate change and natural climate variability. Inter-annual variation of precipitation results from the concurrent variations of precipitation frequency and intensity, understanding of the relative importance of frequency and intensity in the variability of precipitation can help fathom its changing properties. Investigation of the long-term changes of precipitation schemes has been extensively carried out in many regions across the world, however, detailed studies of the relative importance of precipitation frequency and intensity in inter-annual variation of precipitation are still limited, especially in the tropics. Therefore, this study presents a comprehensive framework to investigate the inter-annual variation of precipitation and the dominance of precipitation frequency and intensity in a tropical urban city-state, Singapore, based on long-term (1980-2013) daily precipitation series from 22 rain gauges. First, an iterative Mann-Kendall trend test method is applied to detect long-term trends in precipitation total, frequency and intensity at both annual and seasonal time scales. Then, the relative importance of precipitation frequency and intensity in inducing the inter-annual variation of wet-day precipitation total is analyzed using a dominance analysis method based on linear regression. The results show statistically significant upward trends in wet-day precipitation total, frequency and intensity at annual time scale, however, these trends are not evident during the monsoon seasons. The inter-annual variation of wet-day precipitation is mainly dominated by precipitation intensity for most of the stations at annual time scale and during the Northeast monsoon season. However, during the Southwest monsoon season, the inter-annual variation of wet-day precipitation is mainly dominated by precipitation frequency. These results have

Daily Temperature and Precipitation Data for 518 Russian Meteorological Stations (1881 - 2010)

DOE Data Explorer

Bulygina, O. N. [All-Russian Research Institute of Hydrometeorological Information-World Data Centre; Razuvaev, V. N. [All-Russian Research Institute of Hydrometeorological Information-World Data Centre

2012-01-01

Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information-World Data Center (RIHMI-WDC)). This data exchange has its roots in a bilateral initiative known as the Agreement on Protection of the Environment (Tatusko 1990). CDIAC has partnered with NCDC and RIHMI-WDC since the early 1990s to help make former-USSR climate datasets available to the public. The first former-USSR daily temperature and precipitation dataset released by CDIAC was initially created within the framework of the international cooperation between RIHMI-WDC and CDIAC and was published by CDIAC as NDP-040, consisting of data from 223 stations over the former USSR whose data were published in USSR Meteorological Monthly (Part 1: Daily Data). The database presented here consists of records from 518 Russian stations (excluding the former-USSR stations outside the Russian territory contained in NDP-040), for the most part extending through 2010. Records not extending through 2010 result from stations having closed or else their data were not published in Meteorological Monthly of CIS Stations (Part 1: Daily Data). The database was created from the digital media of the State Data Holding. The station inventory was arrived at using (a) the list of Roshydromet stations that are included in the Global Climate Observation Network (this list was approved by the Head of Roshydromet on 25 March 2004) and (b) the list of Roshydromet benchmark meteorological stations prepared by V.I. Kodratyuk, Head of the Department at Voeikov Main Geophysical Observatory.

Precipitation Indices Low Countries

NASA Astrophysics Data System (ADS)

van Engelen, A. F. V.; Ynsen, F.; Buisman, J.; van der Schrier, G.

2009-09-01

Since 1995, KNMI published a series of books(1), presenting an annual reconstruction of weather and climate in the Low Countries, covering the period AD 763-present, or roughly, the last millennium. The reconstructions are based on the interpretation of documentary sources predominantly and comparison with other proxies and instrumental observations. The series also comprises a number of classifications. Amongst them annual classifications for winter and summer temperature and for winter and summer dryness-wetness. The classification of temperature have been reworked into peer reviewed (2) series (AD 1000-present) of seasonal temperatures and temperature indices, the so called LCT (Low Countries Temperature) series, now incorporated in the Millennium databases. Recently we started a study to convert the dryness-wetness classifications into a series of precipitation; the so called LCP (Low Countries Precipitation) series. A brief outline is given here of the applied methodology and preliminary results. The WMO definition for meteorological drought has been followed being that a period is called wet respectively dry when the amount of precipitation is considerable more respectively less than usual (normal). To gain a more quantitative insight for four locations, geographically spread over the Low Countries area (De Bilt, Vlissingen, Maastricht and Uccle), we analysed the statistics of daily precipitation series, covering the period 1900-present. This brought us to the following definition, valid for the Low Countries: A period is considered as (very) dry respectively (very) wet if over a continuous period of at least 60 days (~two months) cq 90 days (~three months) on at least two out of the four locations 50% less resp. 50% more than the normal amount for the location (based on the 1961-1990 normal period) has been measured. This results into the following classification into five drought classes hat could be applied to non instrumental observations: Very wet period

Study of lithium extraction from brine water, Bledug Kuwu, Indonesia by the precipitation series of oxalic acid and carbonate sodium

NASA Astrophysics Data System (ADS)

Sulistiyono, Eko; Lalasari, Latifa Hanum; Mayangsari, W.; Prasetyo, A. B.

2018-05-01

Lithium is one of the key elements in the development of batteries for electric car applications. Currently, the resources of the world's lithium are derived from brine water and lithium mineral based on spodumene rock. Indonesia which is located in the area of the ring of fire, has potential brine water resources in some area, such as brine water from Bledug Kuwu, Central Java that used in this research. The purposes of this research are to characterize brine water, Bledug Kuwu and to investigate the influence of chemical solvents on Li, Na, K, Ca, Mg, Al, B ion precipitation from brine water. This research was done with 2 times the process of chemical precipitation that runs series as follows: 5 liters of brine water were chemically precipitated using 400 ml of 12.43 N oxalic acid and followed by chemical precipitation using 400 mL of 7.07 N sodium carbonate solutions. Evaporation and filtration processes were also done twice in an effort to separate white precipitate and filtrate. The filtrate was analyzed by ICP-OES and white precipitates (salts) were analyzed by SEM, XRD, and XRF. The result shows that oxalate precipitation process extracted 32.24% Al, 23.42% B, 22.43% Ca, 14.26% Fe, 3.21 % K, 9.86% Na and 14.26% Li, the following process by carbonate precipitation process extracted 98.86% Mg, 73% Ca, 22.53% Li, 82.04% Al, 14.38% B, 12.50% K, 2.27% Na. There is 63.21% lithium is not extracted from the series process. The SEM analysis shows that the structure of granules on the precipitated salts by oxalic acid form gentle cubic-shaped solid. In the other hand, oxalate precipitation followed by sodium carbonate has various particle sizes and the shape of crystals is fragments, prism and cube look like magnesium carbonate, calcium chloride, and calcite's crystal respectively. This is in accordance with XRD analysis that phases of whewellite (CaC2O4.H2O), disodium oxalate (Na2C2O4), magnesite (MgCO3), calcium lithium aluminum (Al1.19 Ca1Li0.81), dolomite (CaCO3

Simulation of daily streamflow for nine river basins in eastern Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Haj, Adel E.; Christiansen, Daniel E.; Hutchinson, Kasey J.

2015-10-14

The accuracy of Precipitation-Runoff Modeling System model streamflow estimates of nine river basins in eastern Iowa as compared to measured values at U.S. Geological Survey streamflow-gaging stations varied. The Precipitation-Runoff Modeling System models of nine river basins in eastern Iowa were satisfactory at estimating daily streamflow at 57 of the 79 calibration sites and 13 of the 14 validation sites based on statistical results. Unsatisfactory performance can be contributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) the availability and accuracy of meteorological input data. The Precipitation- Runoff Modeling System models of nine river basins in eastern Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

Trends and periodicity of daily temperature and precipitation extremes during 1960-2013 in Hunan Province, central south China

NASA Astrophysics Data System (ADS)

Chen, Ajiao; He, Xinguang; Guan, Huade; Cai, Yi

2018-04-01

In this study, the trends and periodicity in climate extremes are examined in Hunan Province over the period 1960-2013 on the basis of 27 extreme climate indices calculated from daily temperature and precipitation records at 89 meteorological stations. The results show that in the whole province, temperature extremes exhibit a warming trend with more than 50% stations being statistically significant for 7 out of 16 temperature indices, and the nighttime temperature increases faster than the daytime temperature at the annual scale. The changes in most extreme temperature indices show strongly coherent spatial patterns. Moreover, the change rates of almost all temperature indices in north Hunan are greater than those of other regions. However, the statistically significant changes in indices of extreme precipitation are observed at fewer stations than in extreme temperature indices, forming less spatially coherent patterns. Positive trends in indices of extreme precipitation show that the amount and intensity of extreme precipitation events are generally increasing in both annual and seasonal scales, whereas the significant downward trend in consecutive wet days indicates that the precipitation becomes more even over the study period. Analysis of changes in probability distributions of extreme indices for 1960-1986 and 1987-2013 also demonstrates a remarkable shift toward warmer condition and increasing tendency in the amount and intensity of extreme precipitation during the past decades. The variations in extreme climate indices exhibit inconstant frequencies in the wavelet power spectrum. Among the 16 temperature indices, 2 of them show significant 1-year periodic oscillation and 7 of them exhibit significant 4-year cycle during some certain periods. However, significant periodic oscillations can be found in all of the precipitation indices. Wet-day precipitation and three absolute precipitation indices show significant 1-year cycle and other seven provide

Regionalization of precipitation characteristics in Iran's Lake Urmia basin

NASA Astrophysics Data System (ADS)

Fazel, Nasim; Berndtsson, Ronny; Uvo, Cintia Bertacchi; Madani, Kaveh; Kløve, Bjørn

2018-04-01

Lake Urmia in northwest Iran, once one of the largest hypersaline lakes in the world, has shrunk by almost 90% in area and 80% in volume during the last four decades. To improve the understanding of regional differences in water availability throughout the region and to refine the existing information on precipitation variability, this study investigated the spatial pattern of precipitation for the Lake Urmia basin. Daily rainfall time series from 122 precipitation stations with different record lengths were used to extract 15 statistical descriptors comprising 25th percentile, 75th percentile, and coefficient of variation for annual and seasonal total precipitation. Principal component analysis in association with cluster analysis identified three main homogeneous precipitation groups in the lake basin. The first sub-region (group 1) includes stations located in the center and southeast; the second sub-region (group 2) covers mostly northern and northeastern part of the basin, and the third sub-region (group 3) covers the western and southern edges of the basin. Results of principal component (PC) and clustering analyses showed that seasonal precipitation variation is the most important feature controlling the spatial pattern of precipitation in the lake basin. The 25th and 75th percentiles of winter and autumn are the most important variables controlling the spatial pattern of the first rotated principal component explaining about 32% of the total variance. Summer and spring precipitation variations are the most important variables in the second and third rotated principal components, respectively. Seasonal variation in precipitation amount and seasonality are explained by topography and influenced by the lake and westerly winds that are related to the strength of the North Atlantic Oscillation. Despite using incomplete time series with different lengths, the identified sub-regions are physically meaningful.

Attributing Historical Changes in Probabilities of Record-Breaking Daily Temperature and Precipitation Extreme Events

DOE PAGES

Shiogama, Hideo; Imada, Yukiko; Mori, Masato; ...

2016-08-07

Here, we describe two unprecedented large (100-member), longterm (61-year) ensembles based on MRI-AGCM3.2, which were driven by historical and non-warming climate forcing. These ensembles comprise the "Database for Policy Decision making for Future climate change (d4PDF)". We compare these ensembles to large ensembles based on another climate model, as well as to observed data, to investigate the influence of anthropogenic activities on historical changes in the numbers of record-breaking events, including: the annual coldest daily minimum temperature (TNn), the annual warmest daily maximum temperature (TXx) and the annual most intense daily precipitation event (Rx1day). These two climate model ensembles indicatemore » that human activity has already had statistically significant impacts on the number of record-breaking extreme events worldwide mainly in the Northern Hemisphere land. Specifically, human activities have altered the likelihood that a wider area globally would suffer record-breaking TNn, TXx and Rx1day events than that observed over the 2001- 2010 period by a factor of at least 0.6, 5.4 and 1.3, respectively. However, we also find that the estimated spatial patterns and amplitudes of anthropogenic impacts on the probabilities of record-breaking events are sensitive to the climate model and/or natural-world boundary conditions used in the attribution studies.« less

Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

NASA Astrophysics Data System (ADS)

Hiebl, Johann; Frei, Christoph

2018-04-01

Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.

Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

NASA Technical Reports Server (NTRS)

Wobus, Cameron; Reynolds, Lara; Jones, Russell; Horton, Radley; Smith, Joel; Fries, J. Stephen; Tryby, Michael; Spero, Tanya; Nolte, Chris

2015-01-01

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

Two case studies on NARCCAP precipitation extremes

NASA Astrophysics Data System (ADS)

Weller, Grant B.; Cooley, Daniel; Sain, Stephan R.; Bukovsky, Melissa S.; Mearns, Linda O.

2013-09-01

We introduce novel methodology to examine the ability of six regional climate models (RCMs) in the North American Regional Climate Change Assessment Program (NARCCAP) ensemble to simulate past extreme precipitation events seen in the observational record over two different regions and seasons. Our primary objective is to examine the strength of daily correspondence of extreme precipitation events between observations and the output of both the RCMs and the driving reanalysis product. To explore this correspondence, we employ methods from multivariate extreme value theory. These methods require that we account for marginal behavior, and we first model and compare climatological quantities which describe tail behavior of daily precipitation for both the observations and model output before turning attention to quantifying the correspondence of the extreme events. Daily precipitation in a West Coast region of North America is analyzed in two seasons, and it is found that the simulated extreme events from the reanalysis-driven NARCCAP models exhibit strong daily correspondence to extreme events in the observational record. Precipitation over a central region of the United States is examined, and we find some daily correspondence between winter extremes simulated by reanalysis-driven NARCCAP models and those seen in observations, but no such correspondence is found for summer extremes. Furthermore, we find greater discrepancies among the NARCCAP models in the tail characteristics of the distribution of daily summer precipitation over this region than seen in precipitation over the West Coast region. We find that the models which employ spectral nudging exhibit stronger tail dependence to observations in the central region.

A new precipitation and meteorological drought climatology based on weather patterns

NASA Astrophysics Data System (ADS)

Richardson, D.; Fowler, H. J.; Kilsby, C. G.; Neal, R.

2017-12-01

Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterise the broad-scale atmospheric circulation over a given region. An analysis of regional UK precipitation and meteorological drought climatology with respect to a set of objectively defined weather patterns is presented. This classification system, introduced last year, is currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. The classification consists of 30 daily patterns derived from North Atlantic Ocean and European mean sea level pressure data. Clustering these 30 patterns yields another set of eight patterns that are intended for use in longer-range applications. Weather pattern definitions and daily occurrences are mapped to the commonly-used Lamb Weather Types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Drought index series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for different drought index thresholds, representing dry, wet and drought conditions. The set of 30 weather patterns is shown to be adequate for precipitation-based analyses in the UK, although the smaller set of clustered patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in the context of precipitation studies. Weather patterns associated with drought over the different UK regions are identified. This has potential forecasting application - if a model (e.g. a global seasonal forecast model) can predict weather pattern occurrences then regional drought outlooks may be derived from the forecasted weather patterns.

Comparison of different synthetic 5-min rainfall time series regarding their suitability for urban drainage modelling

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Callau Poduje, Ana; Müller, Hannes; Shehu, Bora; Haberlandt, Uwe; Lorenz, Manuel; Wagner, Sven; Kunstmann, Harald; Müller, Thomas; Mosthaf, Tobias; Bárdossy, András

2015-04-01

For the design and operation of urban drainage systems with numerical simulation models, long, continuous precipitation time series with high temporal resolution are necessary. Suitable observed time series are rare. As a result, intelligent design concepts often use uncertain or unsuitable precipitation data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic precipitation data for urban drainage modelling are advanced, tested, and compared. The presented study compares four different approaches of precipitation models regarding their ability to reproduce rainfall and runoff characteristics. These include one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model, and one disaggregation approach based on daily precipitation measurements. All four models produce long precipitation time series with a temporal resolution of five minutes. The synthetic time series are first compared to observed rainfall reference time series. Comparison criteria include event based statistics like mean dry spell and wet spell duration, wet spell amount and intensity, long term means of precipitation sum and number of events, and extreme value distributions for different durations. Then they are compared regarding simulated discharge characteristics using an urban hydrological model on a fictitious sewage network. First results show a principal suitability of all rainfall models but with different strengths and weaknesses regarding the different rainfall and runoff characteristics considered.

Detecting daily routines of older adults using sensor time series clustering.

PubMed

Hajihashemi, Zahra; Yefimova, Maria; Popescu, Mihail

2014-01-01

The aim of this paper is to develop an algorithm to identify deviations in patterns of day-to-day activities of older adults to generate alerts to the healthcare providers for timely interventions. Daily routines, such as bathroom visits, can be monitored by automated in-home sensor systems. We present a novel approach that finds periodicity in sensor time series data using clustering approach. For this study, we used data set from TigerPlace, a retirement community in Columbia, MO, where apartments are equipped with a network of motion, pressure and depth sensors. A retrospective multiple case study (N=3) design was used to quantify bathroom visits as parts of the older adult's daily routine, over a 10-day period. The distribution of duration, number, and average time between sensor hits was used to define the confidence level for routine visit extraction. Then, a hierarchical clustering was applied to extract periodic patterns. The performance of the proposed method was evaluated through experimental results.

The Climate Hazards group InfraRed Precipitation (CHIRP) with Stations (CHIRPS): Development and Validation

NASA Astrophysics Data System (ADS)

Peterson, P.; Funk, C. C.; Husak, G. J.; Pedreros, D. H.; Landsfeld, M.; Verdin, J. P.; Shukla, S.

2013-12-01

CHIRP and CHIRPS are new quasi-global precipitation products with daily to seasonal time scales, a 0.05° resolution, and a 1981 to near real-time period of record. Developed by the Climate Hazards Group at UCSB and scientists at the U.S. Geological Survey Earth Resources Observation and Science Center specifically for drought early warning and environmental monitoring, CHIRPS provides moderate latency precipitation estimates that place observed hydrologic extremes in their historic context. Three main types of information are used in the CHIRPS: (1) global 0.05° precipitation climatologies, (2) time-varying grids of satellite-based precipitation estimates, and (3) in situ precipitation observations. CHIRP: The global grids of long-term (1980-2009) average precipitation were estimated for each month based on station data, averaged satellite observations, and physiographic parameters. 1981-present time-varying grids of satellite precipitation were derived from spatially varying regression models based on pentadal cold cloud duration (CCD) values and TRMM V7 training data. The CCD time-series were derived from the CPC and NOAA B1 datasets. Pentadal CCD-percent anomaly values were multiplied by pentadal climatology fields to produce low bias pentadal precipitation estimates. CHIRPS: The CHG station blending procedure uses the satellite-observed spatial covariance structure to assign relative weights to neighboring stations and the CHIRP values. The CHIRPS blending procedure is based on the expected correlation between precipitation at a given target location and precipitation at the locations of the neighboring observation stations. These correlations are estimated using the CHIRP fields. The CHG has developed an extensive archive of in situ daily, pentadal and monthly precipitation totals. The CHG database has over half a billion daily rainfall observations since 1980 and another half billion before 1980. Most of these observations come from four sets of global

A new precipitation and drought climatology based on weather patterns

PubMed Central

Fowler, Hayley J.; Kilsby, Christopher G.; Neal, Robert

2017-01-01

ABSTRACT Weather‐pattern, or weather‐type, classifications are a valuable tool in many applications as they characterize the broad‐scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather‐pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI‐based drought months. The new weather‐pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation‐based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra‐pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification‐based analyses in the UK. PMID:29456290

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

Relationships between interdecadal variability and extreme precipitation events in South America during the monsoon season

NASA Astrophysics Data System (ADS)

Grimm, Alice; Laureanti, Nicole; Rodakoviski, Rodrigo

2016-04-01

events frequency in opposite phases of the interdecadal oscillations display spatial patterns very similar to those of the corresponding modes. In addition, the modes of extreme events frequency bear similarity to the modes of seasonal precipitation, although a complete assessment of this similarity is not possible with the daily data available. The Kolmogorov-Smirnov test is applied to the daily precipitation series for positive and negative phases of the interdecadal modes, in regions with high factor loadings. It shows, with significance level better than 0.01, that daily precipitation from opposite phases pertains to different frequency distributions. Further analyses disclose clearly that there is much greater relative impact of the interdecadal oscillations on the extreme ranges of daily rainfall than in the ranges of moderate and light rainfall. This impact is more linear is spring than in summer. Acknowledgments: This work was supported by: Inter-American Institute for Global Change Research (IAI) CRN3035 which is supported by the US National Science Foundation (Grant GEO-1128040), European Community's Seventh Framework Programme under Grant Agreement n° 212492 (CLARIS LPB), and CNPq-Brazil (National Council for Scientific and Technologic Development).

beta Phase Growth and Precipitation in the 5xxx Series Aluminum Alloy System

NASA Astrophysics Data System (ADS)

Scotto D'Antuono, Daniel

The 5xxx series aluminum alloys are commonly used for structural applications due to their high strength to weight ratio, corrosion resistance, and weldability. This material system is a non-heat treatable aluminum and derives its strength from a super saturation of magnesium (3%>), and from cold rolling. While these materials have many admiral properties, they can undergo a process known as sensitization when exposed to elevated temperatures (50-280°C) for extended periods of time. During this process, magnesium segregates toward the grain boundaries and forms the secondary precipitate β phase (Al3Mg2). When exposed to harsh environments such as sea water, a galvanic couple is formed between the Al matrix and the β phase precipitates. The precipitates become anodic to the matrix and preferentially dissolve leaving gaps along the boundary network, ultimately leading to stress corrosion cracking. While this problem has been known to occur for some time now, questions relating to nucleation sites, misorientation dependence, effect of prior strain, and preferred temperature regimes remain unanswered. The work contained in this thesis attempted to better understand the kinetics, growth, and misorientation dependence, of β phase precipitation using in situ transmission electron microscopy experiments which allowed for direct visualization of the precipitation process. Orientation imaging using a Nanomegas/ASTAR system (OIM in TEM) coupled with the in situ experiments, along with elemental STEM EELs mapping were used to better understand the diffusion of Mg and found low angle boundaries as potential sites for nucleation. The resulting STEM EELs experiments also showed that Mg is much more stable at the grain boundaries than previously thought. Concurrent bulk ex-situ studies were used to compare various heat treatments, as well as to failed in service material showing that the low temperature treatments yield the metastable β’ phase more readily than the �

Risk assessment of precipitation extremes in northern Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

2018-05-01

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

Evolution of Precipitation Extremes in Three Large Ensembles of Climate Simulations - Impact of Spatial and Temporal Resolutions

NASA Astrophysics Data System (ADS)

Martel, J. L.; Brissette, F.; Mailhot, A.; Wood, R. R.; Ludwig, R.; Frigon, A.; Leduc, M.; Turcotte, R.

2017-12-01

Recent studies indicate that the frequency and intensity of extreme precipitation will increase in future climate due to global warming. In this study, we compare annual maxima precipitation series from three large ensembles of climate simulations at various spatial and temporal resolutions. The first two are at the global scale: the Canadian Earth System Model (CanESM2) 50-member large ensemble (CanESM2-LE) at a 2.8° resolution and the Community Earth System Model (CESM1) 40-member large ensemble (CESM1-LE) at a 1° resolution. The third ensemble is at the regional scale over both Eastern North America and Europe: the Canadian Regional Climate Model (CRCM5) 50-member large ensemble (CRCM5-LE) at a 0.11° resolution, driven at its boundaries by the CanESM-LE. The CRCM5-LE is a new ensemble issued from the ClimEx project (http://www.climex-project.org), a Québec-Bavaria collaboration. Using these three large ensembles, change in extreme precipitations over the historical (1980-2010) and future (2070-2100) periods are investigated. This results in 1 500 (30 years x 50 members for CanESM2-LE and CRCM5-LE) and 1200 (30 years x 40 members for CESM1-LE) simulated years over both the historical and future periods. Using these large datasets, the empirical daily (and sub-daily for CRCM5-LE) extreme precipitation quantiles for large return periods ranging from 2 to 100 years are computed. Results indicate that daily extreme precipitations generally will increase over most land grid points of both domains according to the three large ensembles. Regarding the CRCM5-LE, the increase in sub-daily extreme precipitations will be even more important than the one observed for daily extreme precipitations. Considering that many public infrastructures have lifespans exceeding 75 years, the increase in extremes has important implications on service levels of water infrastructures and public safety.

Comparison of three-parameter probability distributions for representing annual extreme and partial duration precipitation series

NASA Astrophysics Data System (ADS)

Wilks, Daniel S.

1993-10-01

Performance of 8 three-parameter probability distributions for representing annual extreme and partial duration precipitation data at stations in the northeastern and southeastern United States is investigated. Particular attention is paid to fidelity on the right tail, through use of a bootstrap procedure simulating extrapolation on the right tail beyond the data. It is found that the beta-κ distribution best describes the extreme right tail of annual extreme series, and the beta-P distribution is best for the partial duration data. The conventionally employed two-parameter Gumbel distribution is found to substantially underestimate probabilities associated with the larger precipitation amounts for both annual extreme and partial duration data. Fitting the distributions using left-censored data did not result in improved fits to the right tail.

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval

Trend analysis of precipitation in Jharkhand State, India. Investigating precipitation variability in Jharkhand State

NASA Astrophysics Data System (ADS)

Chandniha, Surendra Kumar; Meshram, Sarita Gajbhiye; Adamowski, Jan Franklin; Meshram, Chandrashekhar

2017-10-01

Jharkhand is one of the eastern states of India which has an agriculture-based economy. Uncertain and erratic distribution of precipitation as well as a lack of state water resources planning is the major limitation to crop growth in the region. In this study, the spatial and temporal variability in precipitation in the state was examined using a monthly precipitation time series of 111 years (1901-2011) from 18 meteorological stations. Autocorrelation and Mann-Kendall/modified Mann-Kendall tests were utilized to detect possible trends, and the Theil and Sen slope estimator test was used to determine the magnitude of change over the entire time series. The most probable change year (change point) was detected using the Pettitt-Mann-Whitney test, and the entire time series was sub-divided into two parts: before and after the change point. Arc-Map 9.3 software was utilized to assess the spatial patterns of the trends over the entire state. Annual precipitation exhibited a decreasing trend in 5 out of 18 stations during the whole period. For annual, monsoon and winter periods of precipitation, the slope test indicated a decreasing trend for all stations during 1901-2011. The highest variability was observed in post-monsoon precipitation (77.87 %) and the lowest variability was observed in the annual series (15.76 %) over the 111 years. An increasing trend in precipitation in the state was found during the period 1901-1949, which was reversed during the subsequent period (1950-2011).

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Daily Mean Temperature Affects Urolithiasis Presentation in Seoul: a Time-series Analysis.

PubMed

Lee, SeoYeon; Kim, Min-Su; Kim, Jung Hoon; Kwon, Jong Kyou; Chi, Byung Hoon; Kim, Jin Wook; Chang, In Ho

2016-05-01

This study aimed to investigate the overall cumulative exposure-response and the lag response relationships between daily temperature and urolithiasis presentation in Seoul. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of urolithiasis presentation associated with mean daily temperature, including the cumulative RR for a 20 days period, and RR for individual daily lag through 20 days. We analyzed data from 14,518 patients of 4 hospitals emergency department who sought medical evaluation or treatment of urolithiasis from 2005-2013 in Seoul. RR was estimated according to sex and age. Associations between mean daily temperature and urolithiasis presentation were not monotonic. Furthermore, there was variation in the exposure-response curve shapes and the strength of association at different temperatures, although in most cases RRs increased for temperatures above the 13°C reference value. The RRs for urolothiasis at 29°C vs. 13°C were 2.54 in all patients (95% confidence interval [CI]: 1.67-3.87), 2.59 in male (95% CI, 1.56-4.32), 2.42 in female (95% CI, 1.15-5.07), 3.83 in male less than 40 years old (95% CI, 1.78-8.26), and 2.47 in male between 40 and 60 years old (95% CI, 1.15-5.34). Consistent trends of increasing RR of urolithiasis presentation were observed within 5 days of high temperatures across all groups. Urolithiasis presentation increased with high temperature with higher daily mean temperatures, with the strongest associations estimated for lags of only a few days, in Seoul, a metropolitan city in Korea.

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

NASA Astrophysics Data System (ADS)

Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

2014-05-01

Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help

Rainfall frequency analysis for ungauged sites using satellite precipitation products

NASA Astrophysics Data System (ADS)

Gado, Tamer A.; Hsu, Kuolin; Sorooshian, Soroosh

2017-11-01

The occurrence of extreme rainfall events and their impacts on hydrologic systems and society are critical considerations in the design and management of a large number of water resources projects. As precipitation records are often limited or unavailable at many sites, it is essential to develop better methods for regional estimation of extreme rainfall at these partially-gauged or ungauged sites. In this study, an innovative method for regional rainfall frequency analysis for ungauged sites is presented. The new method (hereafter, this is called the RRFA-S) is based on corrected annual maximum series obtained from a satellite precipitation product (e.g., PERSIANN-CDR). The probability matching method (PMM) is used here for bias correction to match the CDF of satellite-based precipitation data with the gauged data. The RRFA-S method was assessed through a comparative study with the traditional index flood method using the available annual maximum series of daily rainfall in two different regions in USA (11 sites in Colorado and 18 sites in California). The leave-one-out cross-validation technique was used to represent the ungauged site condition. Results of this numerical application have found that the quantile estimates obtained from the new approach are more accurate and more robust than those given by the traditional index flood method.

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

NASA Astrophysics Data System (ADS)

Diaconescu, Emilia Paula; Mailhot, Alain; Brown, Ross; Chaumont, Diane

2018-03-01

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980-2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

Adjusted monthly temperature and precipitation values for Guinea Conakry (1941-2010) using HOMER.

NASA Astrophysics Data System (ADS)

Aguilar, Enric; Aziz Barry, Abdoul; Mestre, Olivier

2013-04-01

Africa is a data sparse region and there are very few studies presenting homogenized monthly records. In this work, we introduce a dataset consisting of 12 stations spread over Guinea Conakry containing daily values of maximum and minimum temperature and accumulated rainfall for the period 1941-2010. The daily values have been quality controlled using R-Climdex routines, plus other interactive quality control applications, coded by the authors. After applying the different tests, more than 200 daily values were flagged as doubtful and carefully checked against the statistical distribution of the series and the rest of the dataset. Finally, 40 values were modified or set to missing and the rest were validated. The quality controlled daily dataset was used to produce monthly means and homogenized with HOMER, a new R-pacakge which includes the relative methods that performed better in the experiments conducted in the framework of the COST-HOME action. A total number of 38 inhomogeneities were found for temperature. As a total of 788 years of data were analyzed, the average ratio was one break every 20.7 years. The station with a larger number of inhomogeneities was Conakry (5 breaks) and one station, Kissidougou, was identified as homogeneous. The average number of breaks/station was 3.2. The mean value of the monthly factors applied to maximum (minimum) temperature was 0.17 °C (-1.08 °C) . For precipitation, due to the demand of a denser network to correctly homogenize this variable, only two major inhomogeneities in Conakry (1941-1961, -12%) and Kindia (1941-1976, -10%) were corrected. The adjusted dataset was used to compute regional series for the three variables and trends for the 1941-2010 period. The regional mean has been computed by simply averaging anomalies to 1971-2000 of the 12 time series. Two different versions have been obtained: a first one (A) makes use of the missing values interpolation made by HOMER (so all annual values in the regional series

waterData--An R package for retrieval, analysis, and anomaly calculation of daily hydrologic time series data, version 1.0

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2012-01-01

Hydrologic time series data and associated anomalies (multiple components of the original time series representing variability at longer-term and shorter-term time scales) are useful for modeling trends in hydrologic variables, such as streamflow, and for modeling water-quality constituents. An R package, called waterData, has been developed for importing daily hydrologic time series data from U.S. Geological Survey streamgages into the R programming environment. In addition to streamflow, data retrieval may include gage height and continuous physical property data, such as specific conductance, pH, water temperature, turbidity, and dissolved oxygen. The package allows for importing daily hydrologic data into R, plotting the data, fixing common data problems, summarizing the data, and the calculation and graphical presentation of anomalies.

Utilization of an Enhanced Canonical Correlation Analysis (ECCA) to Predict Daily Precipitation and Temperature in a Semi-Arid Environment

NASA Astrophysics Data System (ADS)

Lopez, S. R.; Hogue, T. S.

2011-12-01

Global climate models (GCMs) are primarily used to generate historical and future large-scale circulation patterns at a coarse resolution (typical order of 50,000 km2) and fail to capture climate variability at the ground level due to localized surface influences (i.e topography, marine, layer, land cover, etc). Their inability to accurately resolve these processes has led to the development of numerous 'downscaling' techniques. The goal of this study is to enhance statistical downscaling of daily precipitation and temperature for regions with heterogeneous land cover and topography. Our analysis was divided into two periods, historical (1961-2000) and contemporary (1980-2000), and tested using sixteen predictand combinations from four GCMs (GFDL CM2.0, GFDL CM2.1, CNRM-CM3 and MRI-CGCM2 3.2a. The Southern California area was separated into five county regions: Santa Barbara, Ventura, Los Angeles, Orange and San Diego. Principle component analysis (PCA) was performed on ground-based observations in order to (1) reduce the number of redundant gauges and minimize dimensionality and (2) cluster gauges that behave statistically similarly for post-analysis. Post-PCA analysis included extensive testing of predictor-predictand relationships using an enhanced canonical correlation analysis (ECCA). The ECCA includes obtaining the optimal predictand sets for all models within each spatial domain (county) as governed by daily and monthly overall statistics. Results show all models maintain mean annual and monthly behavior within each county and daily statistics are improved. The level of improvement highly depends on the vegetation extent within each county and the land-to-ocean ratio within the GCM spatial grid. The utilization of the entire historical period also leads to better statistical representation of observed daily precipitation. The validated ECCA technique is being applied to future climate scenarios distributed by the IPCC in order to provide forcing data for

A comparison of daily precipitation metrics downscaled using SDSM and WRF + WRFDA models over the Iberian Peninsula.

NASA Astrophysics Data System (ADS)

José González-Rojí, Santos; Wilby, Robert L.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

2017-04-01

Downscaling via the Statistical DownScaling Model (SDSM) version 5.2 and two different configurations of the dynamical WRF model (with and without 3DVAR data assimilation) was evaluated for the estimation of daily precipitation over 21 sites across the Iberian Peninsula during the period 2010-2014. Six different strategies were used to calibrate the SDSM model. These options cover (1) use of NCEP/NCAR R1 Reanalysis and (2) ERA Interim data for downscaling predictor variables calibrated with data from periods (3) 1948-2009 (NCEP/NCAR R1) and (4) 1979-2009 (NCEP/NCAR R1 and ERA Interim). Additionally, for the ERA Interim case, two different grid resolutions have been used, (5) 2.5° and (6) 0.75°. On the other side, for the NCEP/NCAR R1 case, only the 2.5° resolution has been used. Configuring the SDSM model in this way allows testing the sensitivity of the results to different origins of the predictors, fit to different calibration periods and use of different reanalysis resolutions. On the other hand, ERA Interim data at the highest resolution was used as the initial/boundary conditions to run WRF simulations with a 15 km x 15 km horizontal resolution over the Iberian Peninsula, for two different configurations. The first experiment (N) was run using the same configuration typically used for numerical downscaling, with information being fed through the boundaries of the domain. The second experiment (D) was run using 3DVAR data assimilation at 00UTC, 06UTC, 12UTC and 18UTC. In both cases, WRF simulations were run over the period 2009-2014, using the first year (2009) as spin-up for the soil model. Results from the WRF N and D runs and comparable SDSM set up for the period 2010-2014 were evaluated using observations from ECA and E-OBS datasets. In each case, model skill was assessed using seven daily precipitation metrics (absolute mean, wet-day intensity, 90th percentile, maximum 5-day total, maximum number of consecutive dry days, fraction of total from heavy

A Two-year Record of Daily Rainfall Isotopes from Fiji: Implications for Reconstructing Precipitation from Speleothem δ18O

NASA Astrophysics Data System (ADS)

Brett, M.; Mattey, D.; Stephens, M.

2015-12-01

Oxygen isotopes in speleothem provide opportunities to construct precisely dated records of palaeoclimate variability, underpinned by an understanding of both the regional climate and local controls on isotopes in rainfall and groundwater. For tropical islands, a potential means to reconstruct past rainfall variability is to exploit the generally high correlation between rainfall amount and δ18O: the 'amount effect'. The GNIP program provides δ18O data at monthly resolution for several tropical Pacific islands but there are few data for precipitation isotopes at daily resolution, for investigating the amount effect over different timescales in a tropical maritime setting. Timescales are important since meteoric water feeding a speleothem has undergone storage and mixing in the aquifer system and understanding how the isotope amount effect is preserved in aquifer recharge has fundamental implications on the interpretation of speleothem δ18O in terms of palaeo-precipitation. The islands of Fiji host speleothem caves. Seasonal precipitation is related to the movement of the South Pacific Convergence Zone, and interannual variations in rainfall are coupled to ENSO behaviour. Individual rainfall events are stratiform or convective, with proximal moisture sources. We have daily resolution isotope data for rainfall collected at the University of the South Pacific in Suva, covering every rain event in 2012 and 2013. δ18O varies between -18‰ and +3‰ with the annual weighted averages at -7.6‰ and -6.8‰ respectively, while total recorded rainfall amount is similar in both years. We shall present analysis of our data compared with GNIP, meteorological data and back trajectory analyses to demonstrate the nature of the relationship between rainfall amount and isotopic signatures over this short timescale. Comparison with GNIP data for 2012-13 will shed light on the origin of the amount effect at monthly and seasonal timescales in convective, maritime, tropical

Use of a Principal Components Analysis for the Generation of Daily Time Series.

NASA Astrophysics Data System (ADS)

Dreveton, Christine; Guillou, Yann

2004-07-01

A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.

Daily Mean Temperature and Urolithiasis Presentation in Six Cities in Korea: Time-Series Analysis.

PubMed

Chi, Byung Hoon; Chang, In Ho; Choi, Se Young; Suh, Dong Churl; Chang, Chong Won; Choi, Yun Jung; Lee, Seo Yeon

2017-06-01

Seasonal variation in urinary stone presentation is well described in the literature. However, previous studies have some limitations. To explore overall cumulative exposure-response and the heterogeneity in the relationships between daily meteorological factors and urolithiasis incidence in 6 major Korean cities, we analyzed data on 687,833 urolithiasis patients from 2009 to 2013 for 6 large cities in Korea: Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan. Using a time-series design and distributing lag nonlinear methods, we estimated the relative risk (RR) of mean daily urolithiasis incidence (MDUI) associated with mean daily meteorological factors, including the cumulative RR for a 20-day period. The estimated location-specific associations were then pooled using multivariate meta-regression models. A positive association was confirmed between MDUI and mean daily temperature (MDT), and a negative association was shown between MDUI and mean daily relative humidity (MDRH) in all cities. The lag effect was within 5 days. The multivariate Cochran Q test for heterogeneity at MDT was 12.35 (P = 0.136), and the related I² statistic accounted for 35.2% of the variability. Additionally, the Cochran Q test for heterogeneity and I² statistic at MDHR were 26.73 (P value = 0.148) and 24.7% of variability in the total group. Association was confirmed between daily temperature, relative humidity and urolithiasis incidence, and the differences in urolithiasis incidence might have been partially attributable to the different frequencies and the ranges in temperature and humidity between cities in Korea. © 2017 The Korean Academy of Medical Sciences.

Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.

PubMed

Ting Zhu; Li Luo; Xinli Zhang; Yingkang Shi; Wenwu Shen

2017-03-01

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Sanders, Michael J.; Markstrom, Steven L.; Regan, R. Steven; Atkinson, R. Dwight

2017-09-15

A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.

Changes in temperature and precipitation extremes observed in Modena, Italy

NASA Astrophysics Data System (ADS)

Boccolari, M.; Malmusi, S.

2013-03-01

Climate changes has become one of the most analysed subjects from researchers community, mainly because of the numerous extreme events that hit the globe. To have a better view of climate changes and trends, long observations time series are needed. During last decade a lot of Italian time series, concerning several surface meteorological variables, have been analysed and published. No one of them includes one of the longest record in Italy, the time series of the Geophysical Observatory of the University of Modena and Reggio Emilia. Measurements, collected since early 19th century, always in the same position, except for some months during the second world war, embrace daily temperature, precipitation amount, relative humidity, pressure, cloudiness and other variables. In this work we concentrated on the analysis of yearly and seasonal trends and climate extremes of temperature, both minimum and maximum, and precipitation time series, for the periods 1861-2010 and 1831-2010 respectively, in which continuous measurements are available. In general, our results confirm quite well those reported by IPCC and in many other studies over Mediterranean area. In particular, we found that minimum temperature has a non significant positive trend of + 0.1 °C per decade considering all the period, the value increases to 0.9 °C per decade for 1981-2010. For maximum temperature we observed a non significant + 0.1 °C trend for all the period, while + 0.8 °C for the last thirty years. On the other hand precipitation is decreasing, -6.3 mm per decade, considering all the analysed period, while the last thirty years are characterised by a great increment of 74.8 mm per decade. For both variables several climate indices have been analysed and they confirm what has been found for minimum and maximum temperatures and precipitation. In particular, during last 30 years frost days and ice days are decreasing, whereas summer days are increasing. During the last 30-year tropical nights

SDCLIREF - A sub-daily gridded reference dataset

NASA Astrophysics Data System (ADS)

Wood, Raul R.; Willkofer, Florian; Schmid, Franz-Josef; Trentini, Fabian; Komischke, Holger; Ludwig, Ralf

2017-04-01

Climate change is expected to impact the intensity and frequency of hydrometeorological extreme events. In order to adequately capture and analyze extreme rainfall events, in particular when assessing flood and flash flood situations, data is required at high spatial and sub-daily resolution which is often not available in sufficient density and over extended time periods. The ClimEx project (Climate Change and Hydrological Extreme Events) addresses the alteration of hydrological extreme events under climate change conditions. In order to differentiate between a clear climate change signal and the limits of natural variability, unique Single-Model Regional Climate Model Ensembles (CRCM5 driven by CanESM2, RCP8.5) were created for a European and North-American domain, each comprising 50 members of 150 years (1951-2100). In combination with the CORDEX-Database, this newly created ClimEx-Ensemble is a one-of-a-kind model dataset to analyze changes of sub-daily extreme events. For the purpose of bias-correcting the regional climate model ensembles as well as for the baseline calibration and validation of hydrological catchment models, a new sub-daily (3h) high-resolution (500m) gridded reference dataset (SDCLIREF) was created for a domain covering the Upper Danube and Main watersheds ( 100.000km2). As the sub-daily observations lack a continuous time series for the reference period 1980-2010, the need for a suitable method to bridge the gap of the discontinuous time series arouse. The Method of Fragments (Sharma and Srikanthan (2006); Westra et al. (2012)) was applied to transform daily observations to sub-daily rainfall events to extend the time series and densify the station network. Prior to applying the Method of Fragments and creating the gridded dataset using rigorous interpolation routines, data collection of observations, operated by several institutions in three countries (Germany, Austria, Switzerland), and the subsequent quality control of the observations

Climatology and Interannual Variability of Quasi-Global Intense Precipitation Using Satellite Observations

NASA Technical Reports Server (NTRS)

Ricko, Martina; Adler, Robert F.; Huffman, George J.

2016-01-01

Climatology and variations of recent mean and intense precipitation over a near-global (50 deg. S 50 deg. N) domain on a monthly and annual time scale are analyzed. Data used to derive daily precipitation to examine the effects of spatial and temporal coverage of intense precipitation are from the current Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7 precipitation product, with high spatial and temporal resolution during 1998 - 2013. Intense precipitation is defined by several different parameters, such as a 95th percentile threshold of daily precipitation, a mean precipitation that exceeds that percentile, or a fixed threshold of daily precipitation value [e.g., 25 and 50 mm day(exp -1)]. All parameters are used to identify the main characteristics of spatial and temporal variation of intense precipitation. High correlations between examined parameters are observed, especially between climatological monthly mean precipitation and intense precipitation, over both tropical land and ocean. Among the various parameters examined, the one best characterizing intense rainfall is a fraction of daily precipitation Great than or equal to 25 mm day(exp. -1), defined as a ratio between the intense precipitation above the used threshold and mean precipitation. Regions that experience an increase in mean precipitation likely experience a similar increase in intense precipitation, especially during the El Nino Southern Oscillation (ENSO) events. Improved knowledge of this intense precipitation regime and its strong connection to mean precipitation given by the fraction parameter can be used for monitoring of intense rainfall and its intensity on a global to regional scale.

Understanding key drivers controlling daily stable isotope variations in precipitation of Costa Rica, Central America

NASA Astrophysics Data System (ADS)

Sanchez-Murillo, Ricardo; Welsh, Kristin; Birkel, Christian; Esquivel-Hernández, Germain; Corrales-Salazar, Jose; Boll, Jan; Brooks, Erin; Roupsard, Olivier; Katchan, Irina; Arce-Mesén, Rafael; Soulsby, Chris; Araguás-Araguás, Luis

2015-04-01

Costa Rica is located on the Central American Isthmus, which receives direct moisture inputs from the Caribbean Sea and the Pacific Ocean. The relatively narrow, but high relief Central American land bridge is characterized by unique mountainous and lowland microclimates. However, only limited knowledge exists about the impact of relief and regional atmospheric circulation patterns on precipitation origin, transport, and isotopic composition in this tropical region. Therefore, the main scope of this study is to identify the key drivers controlling variations in meteoric waters of Costa Rica using stable isotopes based on daily sample collection for the year 2013. The monitoring sites comprise three strategic locations across Costa Rica: Heredia (Central Valley), Turrialba (Caribbean slope), and Caño Seco (South Pacific slope). Sporadic dry season rain is mostly related to isolated enriched events ranging from -5.8‰ d18O up to -0.9‰ d18O. By mid-May, the Intertropical Convergence Zone reaches Costa Rica resulting in a notable depletion in isotope ratios (up to -18.5‰ d18O). HYSPLIT back air mass trajectories indicate the strong influence on the origin and transport of precipitation of two main moisture transport mechanisms, the Caribbean Low Level Jet and the Colombian Low Level Jet as well as localized convection events. Multiple linear regression models constructed based on Random Forests of surface meteorological information and atmospheric sounding profiles suggest that Lifted Condensation Level and surface relative humidity are the main factors controlling isotopic variations. These findings diverge from the recognized 'amount effect' in monthly composite samples across the tropics. Understanding of stable isotope dynamics in tropical precipitation can be used to enhance catchment and groundwater modeling efforts in ungauged basins where scarcity of long-term monitoring data drastically limit current and future water resources management.

18 years of continuous observation of tritium and atmospheric precipitations in Ramnicu Valcea (Romania): A time series analysis.

PubMed

Duliu, Octavian G; Varlam, Carmen; Shnawaw, Muataz Dheyaa

2018-05-16

To get more information on the origin of tritium and to evidence any possible presence of anthropogenic sources, between January 1999 and December 2016, the precipitation level and tritium concentration were monthly recorded and investigated by the Cryogenic Institute of Ramnicu Valcea, Romania. Compared with similar data covering a radius of about 1200 km westward, the measurements gave similar results concerning the time evolution of tritium content and precipitation level for the entire time interval excepting the period between 2009 and 2011 when the tritium concentrations showed a slight increase, most probable due to the activity of neighboring experimental pilot plant for tritium and deuterium separation. Regardless this fact, all data pointed towards a steady tendency of tritium concentrations to decrease with an annual rate of about 1.4 ± 0.05%. The experimental data on precipitation levels and tritium concentrations form two complete time series whose time series analysis showed, at p < 0.01, the presence of a single one-year periodicity whose coincident maximums which correspond to late spring - early summer months suggest the existence of the Spring Leak mechanism with a possible contribution of the soil moisture remobilization during the warm period. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bias correction of daily satellite precipitation data using genetic algorithm

NASA Astrophysics Data System (ADS)

Pratama, A. W.; Buono, A.; Hidayat, R.; Harsa, H.

2018-05-01

Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

A resampling procedure for generating conditioned daily weather sequences

USGS Publications Warehouse

Clark, Martyn P.; Gangopadhyay, Subhrendu; Brandon, David; Werner, Kevin; Hay, Lauren E.; Rajagopalan, Balaji; Yates, David

2004-01-01

A method is introduced to generate conditioned daily precipitation and temperature time series at multiple stations. The method resamples data from the historical record “nens” times for the period of interest (nens = number of ensemble members) and reorders the ensemble members to reconstruct the observed spatial (intersite) and temporal correlation statistics. The weather generator model is applied to 2307 stations in the contiguous United States and is shown to reproduce the observed spatial correlation between neighboring stations, the observed correlation between variables (e.g., between precipitation and temperature), and the observed temporal correlation between subsequent days in the generated weather sequence. The weather generator model is extended to produce sequences of weather that are conditioned on climate indices (in this case the Niño 3.4 index). Example illustrations of conditioned weather sequences are provided for a station in Arizona (Petrified Forest, 34.8°N, 109.9°W), where El Niño and La Niña conditions have a strong effect on winter precipitation. The conditioned weather sequences generated using the methods described in this paper are appropriate for use as input to hydrologic models to produce multiseason forecasts of streamflow.

A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

NASA Astrophysics Data System (ADS)

Roman, J.

2015-12-01

The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

NASA Astrophysics Data System (ADS)

Cermak, Vladimir; Bodri, Louise

2018-01-01

This work examines the causal relationship between the value of the ground-air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, Δ T(GST-SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground-air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground-air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of Δ T(GST-SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground-air temperature relationship.

Recent variations in seasonality of temperature and precipitation in Canada, 1976-95

NASA Astrophysics Data System (ADS)

Whitfield, Paul H.; Bodtker, Karin; Cannon, Alex J.

2002-11-01

A previously reported analysis of rehabilitated monthly temperature and precipitation time series for several hundred stations across Canada showed generally spatially coherent patterns of variation between two decades (1976-85 and 1986-95). The present work expands that analysis to finer time scales and a greater number of stations. We demonstrate how the finer temporal resolution, at 5 day or 11 day intervals, increases the separation between clusters of recent variations in seasonal patterns of temperature and precipitation. We also expand the analysis by increasing the number of stations from only rehabilitated monthly data sets to rehabilitated daily sets, then to approximately 1500 daily observation stations. This increases the spatial density of data and allows a finer spatial resolution of patterns between the two decades. We also examine the success of clustering partial records, i.e. sites where the data record is incomplete. The intent of this study was to be consistent with previous work and explore how greater temporal and spatial detail in the climate data affects the resolution of patterns of recent climate variations. The variations we report for temperature and precipitation are taking place at different temporal and spatial scales. Further, the spatial patterns are much broader than local climate regions and ecozones, indicating that the differences observed may be the result of variations in atmospheric circulation.

Developing GIOVANNI-based Online Prototypes to Intercompare TRMM-Related Global Gridded-Precipitation Products

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Teng, William; Kempler, Steven; Milich, Lenard

2014-01-01

New online prototypes have been developed to extend and enhance the previous effort by facilitating investigation of product characteristics and intercomparison of precipitation products in different algorithms as well as in different versions at different spatial scales ranging from local to global without downloading data and software. Several popular Tropical Rainfall Measuring Mission (TRMM) products and the TRMM Composite Climatology are included. In addition, users can download customized data in several popular formats for further analysis. Examples show product quality problems and differences in several monthly precipitation products. It is seen that differences in daily and monthly precipitation products are distributed unevenly in space and it is necessary to have tools such as those presented here for customized and detailed investigations. A simple time series and two area maps allow the discovery of abnormal values of 3A25 in one of the months. An example shows a V-shaped valley issue in the Version 6 3B43 time series and another example shows a sudden drop in 3A25 monthly rain rate, all of which provide important information when the products are used for long-term trend studies. Future plans include adding more products and statistical functionality in the prototypes.

Spatio-temporal variability of several eco-precipitation indicators in China

NASA Astrophysics Data System (ADS)

Guo, B. B.; Zhang, J.; Wang, F.

2016-12-01

Climate change is expected to have large impacts on the eco-hydrological processes. Precipitation as one of the most important meteorological factors is a significant parameter in ecohydrology. Many studies and precipitation indexes focused on the long-term precipitation variability have been put forward. However, these former studies did not consider the vegetation response and these indexes could not reflect it efficiently. Eco-precipitation indicators reflecting the features and patterns of precipitations and serving as significant input parameters of eco-hydrological models are of paramount significance to the studies of these models. Therefore we proposed 4 important eco-precipitation indicators—Precipitation Variability Index (PVI), Precipitation Occurrence Rate (λ), Mean Precipitation Depth (1/θ) and Annual Precipitation (AP). The PVI index depicts the precipitation variability with a value of zero for perfectly uniform and increases as precipitation events become more sporadic. The λ, 1/θ and AP depict the precipitation frequency, intensity and annual amount, respectively. With large precipitation and vegetation discrepancies, China is selected as a study area. Firstly, these indicators are calculated separately with 55-years (1961-2015) daily precipitation time-series from 693 weather stations in China. Then, the temporal trend is analyzed through Mann-Kendall (MK) test and parametric t-test in annual time scale. Furthermore, the spatial distribution is analyzed through the spatial interpolation tools ANUsplin. The result shows that: (1) 1/θ increased significantly (4.59cm/10yr) while λ decreased significantly (1.54 days/10yr), which means there is an increasing trend of extreme precipitation events; (2)there is a significant downward trend of PVI, which means the rhythm of precipitation has a uniform and concentrated trend; (3) AP increased insignificantly (0.57mm/10yr); and (4)the MK test of these indicators shows that there is saltation of �

Rapid decadal convective precipitation increase over Eurasia during the last three decades of the 20th century.

PubMed

Ye, Hengchun; Fetzer, Eric J; Wong, Sun; Lambrigtsen, Bjorn H

2017-01-01

Convective precipitation-localized, short-lived, intense, and sometimes violent-is at the root of challenges associated with observation, simulation, and prediction of precipitation. The understanding of long-term changes in convective precipitation characteristics and their role in precipitation extremes and intensity over extratropical regions are imperative to future water resource management; however, they have been studied very little. We show that annual convective precipitation total has been increasing astonishingly fast, at a rate of 18.4%/°C, of which 16% is attributable to an increase in convective precipitation occurrence, and 2.4% is attributable to increased daily intensity based on the 35 years of two (combined) historical data sets of 3-hourly synoptic observations and daily precipitation. We also reveal that annual daily precipitation extreme has been increasing at a rate of about 7.4%/°C in convective events only. Concurrently, the overall increase in mean daily precipitation intensity is mostly due to increased convective precipitation, possibly at the expanse of nonconvective precipitation. As a result, transitional seasons are becoming more summer-like as convective becomes the dominant precipitation type that has accompanied higher daily extremes and intensity since the late 1980s. The data also demonstrate that increasing convective precipitation and daily extremes appear to be directly linearly associated with higher atmospheric water vapor accompanying a warming climate over northern Eurasia.

Storm orientation impacts on atmospheric river induced precipitation efficiency

NASA Astrophysics Data System (ADS)

Mehran, A.; Lettenmaier, D. P.

2016-12-01

Atmospheric Rivers (ARs) along the Pacific North coast are often associated with heavy winter precipitation and flooding. We analyze 35 years (1981 2016) of landfalling ARs over a transect along the U.S. West Coast consisting of four river basins from coastal Washington to Southern California (Chehalis, Russian, Santa Ana, and Santa Margarita Rivers) to assess the impact of storm orientation on precipitation rainout efficiency. We define precipitation rainout efficiency as the correlation coefficient between the net integrated vapor transport and precipitation rate. We use 6-hourly climate data from the Climate Forecast System Reanalysis (CFSR) for each of the landfalling ARs. We compute storm orientation from CFSR wind vectors (daily averaged over atmospheric levels between 1000 hPa and 300 hPa) associated with each AR event. We also compute integrated vapor transport (IVT) by multiplying precipitable water by the wind vector and compare with daily averaged precipitation averaged over the river basins, where daily precipitation is taken from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) to evaluate the impact of storm orientation on rainfall efficiency. We calculate the local topographic orientation of each river basin (slope and aspect) from ArcGIS, which we related to storm orientation. To evaluate the impact of storm orientation on rainout efficiency over the Russian River basin (Northern California), we first calculated approaching IVT (for all of AR induced precipitations from 1981 to 2016) and daily averaged precipitation rate. Next, we calculated the correlation coefficient between IVT and precipitation rate (for all AR induced rainouts over the Russian River basin). Finally, by considering the local topographical changes (slope and aspect from ArcGIS) and integrating them into an effective IVT, we compared the correlation coefficients between actual and effective IVT and basin-average precipitation. We find that over the Russian

Data Analysis of GPM Constellation Satellites-IMERG and ERA-Interim precipitation products over West of Iran

NASA Astrophysics Data System (ADS)

Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

2016-04-01

Precipitation is a critical component of the Earth's hydrological cycle. The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Especially in data sparse regions with insufficient radar coverage, satellite information can provide a spatial and temporal context. Nonetheless, evaluation of satellite precipitation is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation. Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards. In situ observations over mountainous areas are mostly limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. One of the newest and blended methods that use multi-satellites and multi-sensors has been developed for estimating global precipitation. The considered data set known as Integrated Multi-satellitE Retrievals (IMERG) for GPM (Global Precipitation Measurement) is routinely produced by the GPM constellation satellites. Moreover, recent efforts have been put into the improvement of the precipitation products derived from reanalysis systems, which has led to significant progress. One of the best and a worldwide used model is developed by the European Centre for Medium Range Weather Forecasts (ECMWF). They have produced global reanalysis daily precipitation, known as ERA-Interim. This study has evaluated one year of precipitation data from the GPM-IMERG and ERA-Interim reanalysis daily time series over West of Iran. IMERG and ERA-Interim yield underestimate the observed values while IMERG underestimated slightly and performed better when precipitation is greater than 10mm. Furthermore, with respect to evaluation of probability of detection (POD), threat score (TS), false alarm ratio (FAR) and probability

Detection of the relationship between peak temperature and extreme precipitation

NASA Astrophysics Data System (ADS)

Yu, Y.; Liu, J.; Zhiyong, Y.

2017-12-01

Under the background of climate change and human activities, the characteristics and pattern of precipitation have changed significantly in many regions. As the political and cultural center of China, the structure and character of precipitation in Jingjinji District has varied dramatically in recent years. In this paper, the daily precipitation data throughout the period 1960-2013 are selected for analyzing the spatial-temporal variability of precipitation. The results indicate that the frequency and intensity of precipitation presents an increasing trend. Based on the precipitation data, the maximum, minimum and mean precipitation in different temporal and spatial scales is calculated respectively. The temporal and spatial variation of temperature is obtained by using statistical methods. The relationship between temperature and precipitation in different range is analyzed. The curve relates daily precipitation extremes with local temperatures has a peak structure, increasing at the low-medium range of temperature variations but decreasing at high temperatures. The relationship between extreme precipitation is stronger in downtown than that in suburbs.

Studying the effect of weather conditions on daily crash counts using a discrete time-series model.

PubMed

Brijs, Tom; Karlis, Dimitris; Wets, Geert

2008-05-01

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

Is convective precipitation increasing? The case of Catalonia

NASA Astrophysics Data System (ADS)

Llasat, M. C.; Marcos, R.; Turco, M.

2012-04-01

A recent work (Turco and Llasat, 2011) has been performed to analyse the trends of the ETCCDI (Expert Team on Climate Change Detection and Indices) precipitation indices in Catalonia (NE Iberian Peninsula) from 1951 to 2003, calculated from a interpolated dataset of daily precipitation, namely SPAIN02, regular at 0.2° horizontal resolution. This work has showed that no general trends at a regional scale have been observed, considering the annual and the seasonal regional values, and only the consecutive dry days index (CDD) at annual scale shows a locally coherent spatial trend pattern. Simultaneously, Llasat et al (2009, 2010) have showed an important increase of flash-flood events in the same region. Although aspects related with vulnerability, exposure and changes in uses of soil have been found as the main responsible of this increase, a major knowledge on the evolution of high rainfall events is mandatory. Heavy precipitation is usually associated to convective precipitation and therefore the analysis of the latter is a good indicator of it. Particularly, in Catalonia, funding was raised to define a parameter, designated as β, related with the greater or lesser convective character of the precipitation (Llasat, 2001). This parameter estimates the contribution of convective precipitation to total precipitation using 1-min or 5-min rainfall intensities usually estimated by rain gauges and it can be also analysed by means of the meteorological radar (Llasat et al, 2007). Its monthly distribution shows a maximum in August, followed by September, which are the months with the major number of flash-floods in Catalonia. This parameter also allows distinguishing between different kinds of precipitation events taking into account the degree of convective contribution. The main problem is the lack of long rainfall rate series that allow analysing trends in convective precipitation. The second one is related with its heterogeneous spatial and temporal distribution. To

Rapid decadal convective precipitation increase over Eurasia during the last three decades of the 20th century

PubMed Central

Ye, Hengchun; Fetzer, Eric J.; Wong, Sun; Lambrigtsen, Bjorn H.

2017-01-01

Convective precipitation—localized, short-lived, intense, and sometimes violent—is at the root of challenges associated with observation, simulation, and prediction of precipitation. The understanding of long-term changes in convective precipitation characteristics and their role in precipitation extremes and intensity over extratropical regions are imperative to future water resource management; however, they have been studied very little. We show that annual convective precipitation total has been increasing astonishingly fast, at a rate of 18.4%/°C, of which 16% is attributable to an increase in convective precipitation occurrence, and 2.4% is attributable to increased daily intensity based on the 35 years of two (combined) historical data sets of 3-hourly synoptic observations and daily precipitation. We also reveal that annual daily precipitation extreme has been increasing at a rate of about 7.4%/°C in convective events only. Concurrently, the overall increase in mean daily precipitation intensity is mostly due to increased convective precipitation, possibly at the expanse of nonconvective precipitation. As a result, transitional seasons are becoming more summer-like as convective becomes the dominant precipitation type that has accompanied higher daily extremes and intensity since the late 1980s. The data also demonstrate that increasing convective precipitation and daily extremes appear to be directly linearly associated with higher atmospheric water vapor accompanying a warming climate over northern Eurasia. PMID:28138545

Time-series Oxygen-18 Precipitation Isoscapes for Canada and the Northern United States

NASA Astrophysics Data System (ADS)

Delavau, Carly J.; Chun, Kwok P.; Stadnyk, Tricia A.; Birks, S. Jean; Welker, Jeffrey M.

2014-05-01

regionalizations, average adjusted-R2 and RMSE (weighted to number of observations within each isotope zone) range from 0.70 - 0.72 and 2.76 - 2.91, respectively, indicating that on average the different spatial groupings perform comparably. Validation weighted R2and RMSE show a larger spread between models and poorer performance, ranging from 0.45 - 0.59 and 3.28 - 3.39, respectively. Additional evaluation of simulated δ18Oppt at each station and inter/intra-annually is conducted to evaluate model performance over various space and time scales. Stepwise regression derived parameterizations indicate the significance of precipitable water content and latitude as predictor variables for all regionalizations. Long-term (1981-2010) annual average δ18Oppt isoscapes are produced for Canada and the northern US, highlighting the differences between regionalization approaches. 95% confidence interval maps are generated to provide an estimate of the uncertainty associated with long-term δ18Oppt simulations. This is the first ever time-series empirical modelling of δ18Oppt for Canada utilizing CNIP data, as well as the first modelling collaboration between the CNIP and USNIP networks. This study is the initial step towards empirically derived time-series δ18Oppt for use in iso-hydrological modelling studies. Methods and results from this research are equally applicable to ecology and forensics as the simulated δ18Oppt isoscapes provide the primary oxygen source for many plants and foodwebs at refined temporal and spatial scales across Canada and the northern US.

Evaluation of wintertime precipitation forecasts over the Australian Snowy Mountains

NASA Astrophysics Data System (ADS)

Huang, Yi; Chubb, Thomas; Sarmadi, Fahimeh; Siems, Steven T.; Manton, Michael J.; Franklin, Charmaine N.; Ebert, Elizabeth

2018-07-01

This study evaluates the Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction system in forecasting precipitation across the Australian Snowy Mountains for two cool seasons. Metrics based on seasonal accumulated and daily precipitation show that the model is able to reproduce the observed domain-mean accumulated precipitation reasonably well (with a slight overestimation), but this is, in part, due to a compensation of various errors. Both the frequency and intensity of the heavy precipitation days (domain-mean daily precipitation >5 mm day-1) are overrepresented, particularly over the complex terrain and high-elevation areas, whereas the frequency of the very light precipitation days (domain-mean daily precipitation <1 mm day-1) is underestimated, primarily over lower-elevation areas both upwind and downwind of the mountains. Most of the precipitation is forecasted by the grid-scale precipitation scheme, with appreciable snowfalls predicted over the high elevations. The model also demonstrates appreciable skill in reproducing the synoptic regimes. The proportion of the forecast precipitation for each regime is comparable to the observations, although the orographic enhancement over the western slopes of the mountains is more pronounced in the forecasts, particularly for the wetter regimes. An examination on the effect of the lower-atmosphere stability suggests that most of the precipitation (50-70% over the high elevations) is produced under the "unblocked" condition, which is diagnosed 31% of the time. The remainder is produced under the "blocked" condition. Combined with a case study, potential sources of error associated with the forecast precipitation biases are also discussed.

Simulation of daily pesticide concentrations from watershed characteristics and monthly climatic data

USGS Publications Warehouse

Vecchia, Aldo V.; Crawford, Charles G.

2006-01-01

A time-series model was developed to simulate daily pesticide concentrations for streams in the coterminous United States. The model was based on readily available information on pesticide use, climatic variability, and watershed charac-teristics and was used to simulate concentrations for four herbicides [atrazine, ethyldipropylthiocarbamate (EPTC), metolachlor, and trifluralin] and three insecticides (carbofuran, ethoprop, and fonofos) that represent a range of physical and chemical properties, application methods, national application amounts, and areas of use in the United States. The time-series model approximates the probability distributions, seasonal variability, and serial correlation characteristics in daily pesticide concentration data from a national network of monitoring stations. The probability distribution of concentrations for a particular pesticide and station was estimated using the Watershed Regressions for Pesticides (WARP) model. The WARP model, which was developed in previous studies to estimate the probability distribution, was based on selected nationally available watershed-characteristics data, such as pesticide use and soil characteristics. Normality transformations were used to ensure that the annual percentiles for the simulated concentrations agree closely with the percentiles estimated from the WARP model. Seasonal variability in the transformed concentrations was maintained by relating the transformed concentration to precipitation and temperature data from the United States Historical Climatology Network. The monthly precipitation and temperature values were estimated for the centroids of each watershed. Highly significant relations existed between the transformed concentrations, concurrent monthly precipitation, and concurrent and lagged monthly temperature. The relations were consistent among the different pesticides and indicated the transformed concentrations generally increased as precipitation increased but the rate of

Nonstationary frequency analysis for the trivariate flood series of the Weihe River

NASA Astrophysics Data System (ADS)

Jiang, Cong; Xiong, Lihua

2016-04-01

Some intensive human activities such as water-soil conservation can significantly alter the natural hydrological processes of rivers. In this study, the effect of the water-soil conservation on the trivariate flood series from the Weihe River located in the Northwest China is investigated. The annual maxima daily discharge, annual maxima 3-day flood volume and annual maxima 5-day flood volume are chosen as the study data and used to compose the trivariate flood series. The nonstationarities in both the individual univariate flood series and the corresponding antecedent precipitation series generating the flood events are examined by the Mann-Kendall trend test. It is found that all individual univariate flood series present significant decreasing trend, while the antecedent precipitation series can be treated as stationary. It indicates that the increase of the water-soil conservation land area has altered the rainfall-runoff relationship of the Weihe basin, and induced the nonstationarities in the three individual univariate flood series. The time-varying moments model based on the Pearson type III distribution is applied to capture the nonstationarities in the flood frequency distribution with the water-soil conservation land area introduced as the explanatory variable of the flood distribution parameters. Based on the analysis for each individual univariate flood series, the dependence structure among the three univariate flood series are investigated by the time-varying copula model also with the water-soil conservation land area as the explanatory variable of copula parameters. The results indicate that the dependence among the trivariate flood series is enhanced by the increase of water-soil conservation land area.

Merging Satellite Precipitation Products for Improved Streamflow Simulations

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Barbetta, S.; Camici, S.; Brocca, L.

2017-12-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. Therefore, we propose to merge SM2RAIN and the widely used TMPA 3B42RT product across Italy for a 6-year period (2010-2015) at daily/0.25deg temporal/spatial scale. Two conceptually different merging techniques are compared to each other and evaluated in terms of different statistical metrics, including hit bias, threat score, false alarm rates, and missed rainfall volumes. The first is based on the maximization of the temporal correlation with a reference dataset, while the second is based on a Bayesian approach, which provides a probabilistic satellite precipitation estimate derived from the joint probability distribution of observations and satellite estimates. The merged precipitation products show a better performance with respect to the parental satellite-based products in terms of categorical

Comparison between weather station data in south-eastern Italy and CRU precipitation datasets

NASA Astrophysics Data System (ADS)

Miglietta, D.

2009-04-01

Monthly precipitation data in south-eastern Italy from 1920 to 2005 have been extensively analyzed. Data were collected in almost 200 weather stations located 10-20km apart from each other and almost uniformly distributed in Puglia and Basilicata regions. Apart from few years around world war II, time series are mostly complete and allow a reliable reconstruction of climate variability in the considered region. Statistically significant trends have been studied by applying the Mann-Kendall test to annual, seasonal and monthly values. A comparison has been made between observations and precipitation data given by the Climate Research Unit (CRU), University of East Anglia, with both low (30') and high (10') space resolution grid. In particular, rainfall records, time series behaviors and annual cycles at each station have been compared to the corresponding CRU data. CRU time series show a large negative trend for winter since 1970. Trend is not significant if the whole 20th century is considered (both for the whole year and for winter only). This might be considered as an evidence of recent acceleration towards increasingly dry conditions. However correlation between CRU data and observations is not very high and large percent errors are present mainly in the mountains regions, where observations show a large annual cycle, with intense precipitation in winter, which is not present in CRU data. To identify trends, therefore observed data are needed, even at monthly scale. In particular observations confirm the overall trend, but also indicate large spatial variability, with locations where precipitation has even increased since 1970. Daily precipitation data coming from a subset of weather stations have also been studied for the same time period. The distributions of maximum annual rainfalls, wet spells and dry spells were analyzed for each station, together with their time series. The tools of statistical analysis of extremes have been used in order to evaluate

Assessment of satellite-based precipitation estimates over Paraguay

NASA Astrophysics Data System (ADS)

Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

2018-04-01

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R 2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

Intraseasonal variability in subtropical South America as depicted by precipitation data

NASA Astrophysics Data System (ADS)

González, P. L. M.; Vera, C. S.; Liebmann, B.; Kiladis, G.

2008-06-01

Daily precipitation data from three stations in subtropical Argentina are used to describe intraseasonal variability (20 90 days) during the austral summer. This variability is compared locally and regionally with that present in outgoing longwave radiation (OLR) data, in order to evaluate the performance of this variable as a proxy for convection in the region. The influence of the intraseasonal activity of the South American Seesaw (SASS) leading convection pattern on precipitation is also explored. Results show that intraseasonal variability explains a significant portion of summer precipitation variance, with a clear maximum in the vicinity of the SASS subtropical center. Correlation analysis reveals that OLR can explain only a small portion of daily precipitation variability, implying that it does not constitute a proper proxy for precipitation on daily timescales. On intraseasonal timescales, though, OLR is able to reproduce the main features of precipitation variability. The dynamical conditions that promote the development of intraseasonal variability in the region are further analyzed for selected summers. Seasons associated with a strong intraseasonal signal in precipitation variability show distinctive wet/dry intraseasonal periods in daily raw data, and are associated with a well defined SASS-like spatial pattern of convection. During these summers, strong large-scale forcing (such as warm El Niño/Southern Oscillation (ENSO) events and/or tropical intraseasonal convective activity), and Rossby-wave-like circulation anomalies extending across the Pacific Ocean, are also observed.

Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis

NASA Astrophysics Data System (ADS)

Yang, Changyuan; Chen, Xinyu; Chen, Renjie; Cai, Jing; Meng, Xia; Wan, Yue; Kan, Haidong

2016-08-01

Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the over-dispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0-7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic.

Investigating NARCCAP Precipitation Extremes via Bivariate Extreme Value Theory (Invited)

NASA Astrophysics Data System (ADS)

Weller, G. B.; Cooley, D. S.; Sain, S. R.; Bukovsky, M. S.; Mearns, L. O.

2013-12-01

We introduce methodology from statistical extreme value theory to examine the ability of reanalysis-drive regional climate models to simulate past daily precipitation extremes. Going beyond a comparison of summary statistics such as 20-year return values, we study whether the most extreme precipitation events produced by climate model simulations exhibit correspondence to the most extreme events seen in observational records. The extent of this correspondence is formulated via the statistical concept of tail dependence. We examine several case studies of extreme precipitation events simulated by the six models of the North American Regional Climate Change Assessment Program (NARCCAP) driven by NCEP reanalysis. It is found that the NARCCAP models generally reproduce daily winter precipitation extremes along the Pacific coast quite well; in contrast, simulation of past daily summer precipitation extremes in a central US region is poor. Some differences in the strength of extremal correspondence are seen in the central region between models which employ spectral nudging and those which do not. We demonstrate how these techniques may be used to draw a link between extreme precipitation events and large-scale atmospheric drivers, as well as to downscale extreme precipitation simulated by a future run of a regional climate model. Specifically, we examine potential future changes in the nature of extreme precipitation along the Pacific coast produced by the pineapple express (PE) phenomenon. A link between extreme precipitation events and a "PE Index" derived from North Pacific sea-surface pressure fields is found. This link is used to study PE-influenced extreme precipitation produced by a future-scenario climate model run.

Hydrologic Evaluation of TRMM Multisatellite Precipitation Analysis for Nanliu River Basin in Humid Southwestern China.

PubMed

Zhao, Yinjun; Xie, Qiongying; Lu, Yuan; Hu, Baoqing

2017-06-01

The accuracy of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) daily accumulated precipitation products (3B42RTV7 and 3B42V7) was evaluated for a small basin (the Nanliu river basin). A direct comparison was performed against gauge observations from a period of 9 years (2000-2009) at temporal and spatial scales. The results show that the temporal-spatial precipitation characteristics of the Nanliu river basin are highly consistent with 3B42V7 relative to 3B42RTV7, with higher correlation coefficient (CC) approximately 0.9 at all temporal scales except for the daily scale and a lower relative bias percentage. 3B42V7 slightly overestimates precipitation at all temporal scales except the yearly scale; it slightly underestimates the precipitation at the daily spatial scale. The results also reveal that the precision of TMPA products increases with longer time-aggregated data, and the detection capability of daily TMPA precipitation products are enhanced by augmentation with daily precipitation rates. In addition, daily TMPA products were input into the Xin'anjiang hydrologic model; the results show that 3B42V7-based simulated outputs were well in line with actual stream flow observations, with a high CC (0.90) and Nash-Sutcliffe efficiency coefficient (NSE, 0.79), and the results adequately captured the pattern of the observed flow curve.

Measurement of acid precipitation in Norway

Treesearch

Arne Semb

1976-01-01

Since January 1972, chemical analysis of daily precipitation samples from about 20 background stations in Norway has been carried out on a routine basis. Air monitoring is carried out at six stations. The chemical analysis programme is: sulphate, pH and acidity in precipitation, sulphates and sulphur dioxide in air. In addition, more detailed chemical analysis of...

Climate signature of Northwest U.S. precipitation Extremes

NASA Astrophysics Data System (ADS)

Kushnir, Y.; Nakamura, J.

2017-12-01

The climate signature of precipitation extremes in the Northwest U.S. - the region that includes Oregon, Washington, Idaho, Montana and Wyoming - is studied using composite analysis of atmospheric fields leading to and associated with extreme rainfall events. A K-Medoids cluster analysis is applied to winter (November-February) months, maximum 5-day precipitation amounts calculated from 1-degree gridded daily rainfall between 1950/51 and 2013/14. The clustering divides the region into three sub-regions: one over the far eastern part of the analysis domain, includeing most of Montana and Wyoming. Two other sub-regions are in the west, lying north and south of the latitude of 45N. Using the time series corresponding to the Medoid centers, we extract the largest (top 5%) monthly extreme events to form the basis for the composite analysis. The main circulation feature distinguishing a 5-day extreme precipitation event in the two western sub-regions of the Northwest is the presence of a large, blocking, high pressure anomaly over the Gulf of Alaska about a week before the beginning of the 5-day intense precipitation event. The high pressure center intensifies considerably with time, drifting slowly westward, up to a day before the extreme event. During that time, a weak low pressure centers appears at 30N, to the southwest of the high, deepening as it moves east. As the extreme rainfall event is about to begin, the now deep low is encroaching on the Northwest coast while its southern flank taps well south into the subtropical Pacific, drawing moisture from as south as 15N. During the 5-day extreme precipitation event the high pressure center moves west and weakens while the now intense low converges large amounts of subtropical moisture to precipitate over the western Northwest. The implication of this analysis for extended range prediction is assessed.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE PAGES

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; ...

2016-03-16

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable

High resolution reconstruction of monthly autumn and winter precipitation of Iberian Peninsula for last 150 years.

NASA Astrophysics Data System (ADS)

Cortesi, N.; Trigo, R.; González-Hidalgo, J. C.; Ramos, A.

2012-04-01

Precipitation over Iberian Peninsula (IP) presents large values of interannual variability and large spatial contrasts between wet mountainous regions in the north and dry regions in the southern plains. Unlike other European regions, IP was poorly monitored for precipitation during 19th century. Here we present a new approach to fill this gap. A set of 26 atmospheric circulation weather types (Trigo R.M. and DaCamara C.C., 2000) derived from a recent SLP dataset, the EMULATE (European and North Atlantic daily to multidecadal climate variability) Project, was used to reconstruct Iberian monthly precipitation from October to March during 1851-1947. Principal Component Regression Analysis was chosen to develop monthly precipitation reconstruction back to 1851 and calibrated over 1948-2003 period for 3030 monthly precipitation series of high-density homogenized MOPREDAS (Monthly Precipitation Database for Spain and Portugal) database. Validation was conducted over 1920-1947 at 15 key site locations. Results show high model performance for selected months, with a mean coefficient of variation (CV) around 0.6 during validation period. Lower CV values were achieved in western area of IP. Trigo, R. M., and DaCamara, C.C., 2000: "Circulation weather types and their impact on the precipitation regime in Portugal". Int. J. Climatol., 20, 1559-1581.

Quantifying chemical weathering rates along a precipitation gradient on Basse-Terre Island, French Guadeloupe: New insight from U-series isotopes in weathering rinds

NASA Astrophysics Data System (ADS)

Engel, Jacqueline M.; Ma, Lin; Sak, Peter B.; Gaillardet, Jerome; Ren, Minghua; Engle, Mark A.; Brantley, Susan L.

2016-12-01

Inside soil and saprolite, rock fragments can form weathering clasts (alteration rinds surrounding an unweathered core) and these weathering rinds provide an excellent field system for investigating the initiation of weathering and long term weathering rates. Recently, uranium-series (U-series) disequilibria have shown great potential for determining rind formation rates and quantifying factors controlling weathering advance rates in weathering rinds. To further investigate whether the U-series isotope technique can document differences in long term weathering rates as a function of precipitation, we conducted a new weathering rind study on tropical volcanic Basse-Terre Island in the Lesser Antilles Archipelago. In this study, for the first time we characterized weathering reactions and quantified weathering advance rates in multiple weathering rinds across a steep precipitation gradient. Electron microprobe (EMP) point measurements, bulk major element contents, and U-series isotope compositions were determined in two weathering clasts from the Deshaies watershed with mean annual precipitation (MAP) = 1800 mm and temperature (MAT) = 23 °C. On these clasts, five core-rind transects were measured for locations with different curvature (high, medium, and low) of the rind-core boundary. Results reveal that during rind formation the fraction of elemental loss decreases in the order: Ca ≈ Na > K ≈ Mg > Si ≈ Al > Zr ≈ Ti ≈ Fe. Such observations are consistent with the sequence of reactions after the initiation of weathering: specifically, glass matrix and primary minerals (plagioclase, pyroxene) weather to produce Fe oxyhydroxides, gibbsite and minor kaolinite. Uranium shows addition profiles in the rind due to the infiltration of U-containing soil pore water into the rind as dissolved U phases. U is then incorporated into the rind as Fe-Al oxides precipitate. Such processes lead to significant U-series isotope disequilibria in the rinds. This is the first time

Atmospheric circulation types and extreme areal precipitation in southern central Europe

NASA Astrophysics Data System (ADS)

Jacobeit, Jucundus; Homann, Markus; Philipp, Andreas; Beck, Christoph

2017-04-01

Gridded daily rainfall data for southern central Europe are aggregated to regions of similar precipitation variability by means of S-mode principal component analyses separately for the meteorological seasons. Atmospheric circulation types (CTs) are derived by a particular clustering technique including large-scale fields of SLP, vertical wind and relative humidity at the 700 hPa level as well as the regional rainfall time series. Multiple regression models with monthly CT frequencies as predictors are derived for monthly frequencies and amounts of regional precipitation extremes (beyond the 95 % percentile). Using predictor output from different global climate models (ECHAM6, ECHAM5, EC-EARTH) for different scenarios (RCP4.5, RCP8.5, A1B) and two projection periods (2021-2050, 2071-2100) leads to assessments of future changes in regional precipitation extremes. Most distinctive changes are indicated for the summer season with mainly increasing extremes for the earlier period and widespread decreasing extremes towards the end of the 21st century, mostly for the strong scenario. Considerable uncertainties arise from the predictor use of different global climate models, especially during the winter and spring seasons.

Scaling properties of Polish rain series

NASA Astrophysics Data System (ADS)

Licznar, P.

2009-04-01

implementation of double trace moment method allowed for estimation of local universal multifractal rainfall parameters (α=0.69; C1=0.34; H=-0.01). The research proved the fractal character of rainfall process support and multifractal character of the rainfall intensity values variability among analyzed time series. It is believed that scaling of local Wroclaw's rainfalls for timescales at the range from 24 hours up to 5 minutes opens the door for future research concerning for example random cascades implementation for daily precipitation totals disaggregation for smaller time intervals. The results of such a random cascades functioning in a form of 5 minute artificial rainfall scenarios could be of great practical usability for needs of urban hydrology, and design and hydrodynamic modeling of storm water and combined sewage conveyance systems.

Extreme Precipitation in Poland in the Years 1951-2010

NASA Astrophysics Data System (ADS)

Malinowska, Miroslawa

2017-12-01

The characteristics of extreme precipitation, including the dominant trends, were analysed for eight stations located in different parts of Poland for the period 1951-2010. Five indices enabling the assessment of the intensity and frequency of both extremely dry and wet conditions were applied. The indices included the number of days with precipitation ≥10mm·d-1 (R10), maximum number of consecutive dry days (CDD), maximum 5-day precipitation total (R5d), simple daily intensity index (SDII), and the fraction of annual total precipitation due to events exceeding the 95th percentile calculated for the period 1961-1990. Annual trends were calculated using standard linear regression method, while the fit of the model was assessed with the F-test at the 95% confidence level. The analysed changes in extreme precipitation showed mixed patterns. A significant positive trend in the number of days with precipitation ≥10mm·d-1 (R10) was observed in central Poland, while a significant negative one, in south-eastern Poland. Based on the analysis of maximum 5-day precipitation totals (R5d), statistically significant positive trends in north-western, western and eastern parts of the country were detected, while the negative trends were found in the central and northeastern parts. Daily precipitation, expressed as single daily intensity index (SDII), increased over time in northern and central Poland. In southern Poland, the variation of SDII index showed non-significant negative tendencies. Finally, the fraction of annual total precipitation due to the events exceeding the 1961-1990 95th percentile increased at one station only, namely, in Warsaw. The indicator which refers to dry conditions, i.e. maximum number of consecutive dry days (CDD) displayed negative trends throughout the surveyed area, with the exception of Szczecin that is a representative of north-western Poland.

Influence of Sub-Daily Variation on Multi-Fractal Detrended Fluctuation Analysis of Wind Speed Time Series

PubMed Central

Li, Weinan; Kong, Yanjun; Cong, Xiangyu

2016-01-01

Using multi-fractal detrended fluctuation analysis (MF-DFA), the scaling features of wind speed time series (WSTS) could be explored. In this paper, we discuss the influence of sub-daily variation, which is a natural feature of wind, in MF-DFA of WSTS. First, the choice of the lower bound of the segment length, a significant parameter of MF-DFA, was studied. The results of expanding the lower bound into sub-daily scope shows that an abrupt declination and discrepancy of scaling exponents is caused by the inability to keep the whole diel process of wind in one single segment. Additionally, the specific value, which is effected by the sub-daily feature of local meteo-climatic, might be different. Second, the intra-day temporal order of wind was shuffled to determine the impact of diel variation on scaling exponents of MF-DFA. The results illustrate that disregarding diel variation leads to errors in scaling. We propose that during the MF-DFA of WSTS, the segment length should be longer than 1 day and the diel variation of wind should be maintained to avoid abnormal phenomena and discrepancy in scaling exponents. PMID:26741491

Frequency analysis and its spatiotemporal characteristics of precipitation extreme events in China during 1951-2010

NASA Astrophysics Data System (ADS)

Shao, Yuehong; Wu, Junmei; Ye, Jinyin; Liu, Yonghe

2015-08-01

This study investigates frequency analysis and its spatiotemporal characteristics of precipitation extremes based on annual maximum of daily precipitation (AMP) data of 753 observation stations in China during the period 1951-2010. Several statistical methods including L-moments, Mann-Kendall test (MK test), Student's t test ( t test) and analysis of variance ( F-test) are used to study different statistical properties related to frequency and spatiotemporal characteristics of precipitation extremes. The results indicate that the AMP series of most sites have no linear trends at 90 % confidence level, but there is a distinctive decrease trend in Beijing-Tianjin-Tangshan region. The analysis of abrupt changes shows that there are no significant changes in most sites, and no distinctive regional patterns within the mutation sites either. An important innovation different from the previous studies is the shift in the mean and the variance which are also studied in this paper in order to further analyze the changes of strong and weak precipitation extreme events. The shift analysis shows that we should pay more attention to the drought in North China and to the flood control and drought in South China, especially to those regions that have no clear trend and have a significant shift in the variance. More important, this study conducts the comprehensive analysis of a complete set of quantile estimates and its spatiotemporal characteristic in China. Spatial distribution of quantile estimation based on the AMP series demonstrated that the values gradually increased from the Northwest to the Southeast with the increment of duration and return period, while the increasing rate of estimation is smooth in the arid and semiarid region and is rapid in humid region. Frequency estimates of 50-year return period are in agreement with the maximum observations of AMP series in the most stations, which can provide more quantitative and scientific basis for decision making.

The estimation of probable maximum precipitation: the case of Catalonia.

PubMed

Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel

2008-12-01

A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

NASA Astrophysics Data System (ADS)

Mortuza, M. R.; Demissie, Y.; Li, H. Y.

2014-12-01

Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.

Spatiotemporal variations in precipitation across the Chinese Mongolian plateau over the past half century

NASA Astrophysics Data System (ADS)

Gao, Ruizhong; Li, Fengling; Wang, Xixi; Liu, Tingxi; Du, Dandan; Bai, Yong

2017-09-01

Precipitation, as affected by climate change, controls the growth of steppe grasses and grassland degradation/desertification in semiarid/arid regions, including the Chinese Mongolian plateau. This study examined the spatial variability and temporal trends in precipitation across the plateau in terms of four indexes: total precipitation (P), number of rainy days (Wd), number of precipitation events (N), and average precipitation intensity (Imean). Although seldom published in the literature, this information is vital for efforts to develop adaptive measures to sustain this vulnerable pasture economy. Seven hundred time series were formulated by preprocessing the data on daily precipitation over the period 1960 to 2012 at 25 weather stations scattered across the plateau. The results indicated that although the plateau was becoming drier overall, the intensity of storm events increased markedly, as indicated by decreasing trends for P, Wd and N but an increasing trend for Imean. On average, P decreased by 0.65 mm yr- 1 over the study period, while Imean increased by 0.2 mm d- 1 yr- 1. Across the plateau, the western part was becoming wetter, while the central-eastern part was becoming drier. This spatial discrepancy in the precipitation trends was particularly obvious in the winter dry season, with Imean tending to increase more rapidly in the central-eastern than western part, especially in the spring dry season. It is expected that these trends will continue, thus further challenging the already vulnerable eco-environment of the plateau.

Characterization of increased persistence and intensity of precipitation in the northeastern United States

NASA Astrophysics Data System (ADS)

Guilbert, Justin; Betts, Alan K.; Rizzo, Donna M.; Beckage, Brian; Bomblies, Arne

2015-03-01

We present evidence of increasing persistence in daily precipitation in the northeastern United States that suggests that global circulation changes are affecting regional precipitation patterns. Meteorological data from 222 stations in 10 northeastern states are analyzed using Markov chain parameter estimates to demonstrate that a significant mode of precipitation variability is the persistence of precipitation events. We find that the largest region-wide trend in wet persistence (i.e., the probability of precipitation in 1 day and given precipitation in the preceding day) occurs in June (+0.9% probability per decade over all stations). We also find that the study region is experiencing an increase in the magnitude of high-intensity precipitation events. The largest increases in the 95th percentile of daily precipitation occurred in April with a trend of +0.7 mm/d/decade. We discuss the implications of the observed precipitation signals for watershed hydrology and flood risk.

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is

Evaporation, precipitation, and associated salinity changes at a humid, subtropical estuary

USGS Publications Warehouse

Sumner, D.M.; Belaineh, G.

2005-01-01

The distilling effect of evaporation and the diluting effect of precipitation on salinity at two estuarine sites in the humid subtropical setting of the Indian River Lagoon, Florida, were evaluated based on daily evaporation computed with an energy-budget method and measured precipitation. Despite the larger magnitude of evaporation (about 1,580 mm yr-1) compared to precipitation (about 1,180 mm yr-1) between February 2002 and January 2004, the variability of monthly precipitation induced salinity changes was more than twice the variability of evaporation induced changes. Use of a constant, mean value of evaporation, along with measured values of daily precipitation, were sufficient to produce simulated salinity changes that contained little monthly (root-mean-square error = 0.33??? mo-1 and 0.52??? mo-1 at the two sites) or cumulative error (<1??? yr-1) compared to simulations that used computed daily values of evaporation. This result indicates that measuring the temporal variability in evaporation may not be critical to simulation of salinity within the lagoon. Comparison of evaporation and precipitation induced salinity changes with measured salinity changes indicates that evaporation and precipitation explained only 4% of the changes in salinity within a flow-through area of the lagoon; surface water and ocean inflows probably accounted for most of the variability in salinity at this site. Evaporation and precipitation induced salinity changes explained 61% of the variability in salinity at a flow-restricted part of the lagoon. ?? 2005 Estuarine Research Federation.

Effects of simulated daily precipitation patterns on annual plant populations depend on life stage and climatic region.

PubMed

Köchy, Martin

2008-03-27

To improve the understanding of consequences of climate change for annual plant communities, I used a detailed, grid-based model that simulates the effect of daily rainfall variability on individual plants in five climatic regions on a gradient from 100 to 800 mm mean annual precipitation (MAP). The model explicitly considers moisture storage in the soil. I manipulated daily rainfall variability by changing the daily mean rain (DMR, rain volume on rainy days averaged across years for each day of the year) by +/- 20%. At the same time I adjusted intervals appropriately between rainy days for keeping the mean annual volume constant. In factorial combination with changing DMR I also changed MAP by +/- 20%. Increasing MAP generally increased water availability, establishment, and peak shoot biomass. Increasing DMR increased the time that water was continuously available to plants in the upper 15 to 30 cm of the soil (longest wet period, LWP). The effect of DMR diminished with increasing humidity of the climate. An interaction between water availability and density-dependent germination increased the establishment of seedlings in the arid region, but in the more humid regions the establishment of seedlings decreased with increasing DMR. As plants matured, competition among individuals and their productivity increased, but the size of these effects decreased with the humidity of the regions. Therefore, peak shoot biomass generally increased with increasing DMR but the effect size diminished from the semiarid to the mesic Mediterranean region. Increasing DMR reduced via LWP the annual variability of biomass in the semiarid and dry Mediterranean regions. More rainstorms (greater DMR) increased the recharge of soil water reservoirs in more arid sites with consequences for germination, establishment, productivity, and population persistence. The order of magnitudes of DMR and MAP overlapped partially so that their combined effect is important for projections of climate

Contributions of Dynamic and Thermodynamic Scaling in Subdaily Precipitation Extremes in India

NASA Astrophysics Data System (ADS)

Ali, Haider; Mishra, Vimal

2018-03-01

Despite the importance of subdaily precipitation extremes for urban areas, the role of dynamic and thermodynamic scaling in changes in precipitation extremes in India remains poorly constrained. Here we estimate contributions from thermodynamic and dynamic scaling on changes in subdaily precipitation extremes for 23 urban locations in India. Subdaily precipitation extremes have become more intense during the last few decades. Moreover, we find a twofold rise in the frequency of subdaily precipitation extremes during 1979-2015, which is faster than the increase in daily precipitation extremes. The contribution of dynamic scaling in this rise in the frequency and intensity of subdaily precipitation extremes is higher than the thermodynamic scaling. Moreover, half-hourly precipitation extremes show higher contributions from the both thermodynamic ( 10%/K) and dynamic ( 15%/K) scaling than daily (6%/K and 9%/K, respectively) extremes indicating the role of warming on the rise in the subdaily precipitation extremes in India. Our findings have implications for better understanding the dynamic response of precipitation extremes under the warming climate over India.

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

2015-04-01

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue Downscaling method for Hydrology) statistical downscaling method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an ensemble of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are

Daily water and sediment discharges from selected rivers of the eastern United States; a time-series modeling approach

USGS Publications Warehouse

Fitzgerald, Michael G.; Karlinger, Michael R.

1983-01-01

Time-series models were constructed for analysis of daily runoff and sediment discharge data from selected rivers of the Eastern United States. Logarithmic transformation and first-order differencing of the data sets were necessary to produce second-order, stationary time series and remove seasonal trends. Cyclic models accounted for less than 42 percent of the variance in the water series and 31 percent in the sediment series. Analysis of the apparent oscillations of given frequencies occurring in the data indicates that frequently occurring storms can account for as much as 50 percent of the variation in sediment discharge. Components of the frequency analysis indicate that a linear representation is reasonable for the water-sediment system. Models that incorporate lagged water discharge as input prove superior to univariate techniques in modeling and prediction of sediment discharges. The random component of the models includes errors in measurement and model hypothesis and indicates no serial correlation. An index of sediment production within or between drain-gage basins can be calculated from model parameters.

Occurrence analysis of daily rainfalls by using non-homogeneous Poissonian processes

NASA Astrophysics Data System (ADS)

Sirangelo, B.; Ferrari, E.; de Luca, D. L.

2009-09-01

different geographical zones of Mediterranean area. Time series have been selected on the basis of the availability of at least 50 years in the time period 1921-1985, chosen as calibration period, and of all the years of observation in the subsequent validation period 1986-2005, whose daily rainfall occurrence process variability is under hypothesis. Firstly, for each time series and for each fixed threshold value, parameters estimation of the non-homogeneous Poisson model is carried out, referred to calibration period. As second step, in order to test the hypothesis that daily rainfall occurrence process preserves the same behaviour in more recent time periods, the intensity distribution evaluated for calibration period is also adopted for the validation period. Starting from this and using a Monte Carlo approach, 1000 synthetic generations of daily rainfall occurrences, of length equal to validation period, have been carried out, and for each simulation sample ?(t) has been evaluated. This procedure is adopted because of the complexity of determining analytical statistical confidence limits referred to the sample intensity ?(t). Finally, sample intensity, theoretical function of the calibration period and 95% statistical band, evaluated by Monte Carlo approach, are matching, together with considering, for each threshold value, the mean square error (MSE) between the theoretical ?(t) and the sample one of recorded data, and his correspondent 95% one tail statistical band, estimated from the MSE values between the sample ?(t) of each synthetic series and the theoretical one. The results obtained may be very useful in the context of the identification and calibration of stochastic rainfall models based on historical precipitation data. Further applications of the non-homogeneous Poisson model will concern the joint analyses of the storm occurrence process with the rainfall height marks, interpreted by using a temporally homogeneous model in proper sub-year intervals.

The climatic characteristics of extreme precipitations for short-term intervals in the watershed of Lake Maggiore

NASA Astrophysics Data System (ADS)

Saidi, Helmi; Ciampittiello, Marzia; Dresti, Claudia; Ghiglieri, Giorgio

2013-07-01

Alpine and Mediterranean areas are undergoing a profound change in the typology and distribution of rainfall. In particular, there has been an increase in consecutive non-rainy days, and an escalation of extreme rainy events. The climatic characteristic of extreme precipitations over short-term intervals is an object of study in the watershed of Lake Maggiore, the second largest freshwater basin in Italy (located in the north-west of the country) and an important resource for tourism, fishing and commercial flower growing. The historical extreme rainfall series with high-resolution from 5 to 45 min and above: 1, 2, 3, 6, 12 and 24 h collected at different gauges located at representative sites in the watershed of Lake Maggiore, have been computed to perform regional frequency analysis of annual maxima precipitation based on the L-moments approach, and to produce growth curves for different return-period rainfall events. Because of different rainfall-generating mechanisms in the watershed of Lake Maggiore such as elevation, no single parent distribution could be found for the entire study area. This paper concerns an investigation designed to give a first view of the temporal change and evolution of annual maxima precipitation, focusing particularly on both heavy and extreme events recorded at time intervals ranging from few minutes to 24 h and also to create and develop an extreme storm precipitation database, starting from historical sub-daily precipitation series distributed over the territory. There have been two-part changes in extreme rainfall events occurrence in the last 23 years from 1987 to 2009. Little change is observed in 720 min and 24-h precipitations, but the change seen in 5, 10, 15, 20, 30, 45, 60, 120, 180 and 360 min events is significant. In fact, during the 2000s, growth curves have flattened and annual maxima have decreased.

Linkage Between Hourly Precipitation Events and Atmospheric Temperature Changes over China during the Warm Season

PubMed Central

Miao, Chiyuan; Sun, Qiaohong; Borthwick, Alistair G. L.; Duan, Qingyun

2016-01-01

We investigated changes in the temporospatial features of hourly precipitation during the warm season over mainland China. The frequency and amount of hourly precipitation displayed latitudinal zonation, especially for light and moderate precipitation, which showed successive downward change over time in northeastern and southern China. Changes in the precipitation amount resulted mainly from changes in frequency rather than changes in intensity. We also evaluated the linkage between hourly precipitation and temperature variations and found that hourly precipitation extreme was more sensitive to temperature than other categories of precipitation. A strong dependency of hourly precipitation on temperature occurred at temperatures colder than the median daily temperature; in such cases, regression slopes were greater than the Clausius-Clapeyron (C-C) relation of 7% per degree Celsius. Regression slopes for 31.6%, 59.8%, 96.9%, and 99.1% of all stations were greater than 7% per degree Celsius for the 75th, 90th, 99th, and 99.9th percentiles for precipitation, respectively. The mean regression slopes within the 99.9th percentile of precipitation were three times the C-C rate. Hourly precipitation showed a strong negative relationship with daily maximum temperature and the diurnal temperature range at most stations, whereas the equivalent correlation for daily minimum temperature was weak. PMID:26931350

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

NASA Technical Reports Server (NTRS)

Savtchenko, Andrey; Huffman, George; Meyer, David; Vollmer, Bruce

2018-01-01

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple “phase learning†from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

A lengthy look at the daily grind: time series analysis of events, mood, stress, and satisfaction.

PubMed

Fuller, Julie A; Stanton, Jeffrey M; Fisher, Gwenith G; Spitzmuller, Christiane; Russell, Steven S; Smith, Patricia C

2003-12-01

The present study investigated processes by which job stress and satisfaction unfold over time by examining the relations between daily stressful events, mood, and these variables. Using a Web-based daily survey of stressor events, perceived strain, mood, and job satisfaction completed by 14 university workers, 1,060 occasions of data were collected. Transfer function analysis, a multivariate version of time series analysis, was used to examine the data for relationships among the measured variables after factoring out the contaminating influences of serial dependency. Results revealed a contrast effect in which a stressful event associated positively with higher strain on the same day and associated negatively with strain on the following day. Perceived strain increased over the course of a semester for a majority of participants, suggesting that effects of stress build over time. Finally, the data were consistent with the notion that job satisfaction is a distal outcome that is mediated by perceived strain. ((c) 2003 APA, all rights reserved)

Observation-Corrected Precipitation Estimates in GEOS-5

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing

2014-01-01

Several GEOS-5 applications, including the GEOS-5 seasonal forecasting system and the MERRA-Land data product, rely on global precipitation data that have been corrected with satellite and or gauge-based precipitation observations. This document describes the methodology used to generate the corrected precipitation estimates and their use in GEOS-5 applications. The corrected precipitation estimates are derived by disaggregating publicly available, observationally based, global precipitation products from daily or pentad totals to hourly accumulations using background precipitation estimates from the GEOS-5 atmospheric data assimilation system. Depending on the specific combination of the observational precipitation product and the GEOS-5 background estimates, the observational product may also be downscaled in space. The resulting corrected precipitation data product is at the finer temporal and spatial resolution of the GEOS-5 background and matches the observed precipitation at the coarser scale of the observational product, separately for each day (or pentad) and each grid cell.

A precipitation organization climatology for North Carolina: Development and GIS-based analysis

NASA Astrophysics Data System (ADS)

Zarzar, Christopher M.

A climatology of precipitation organization is developed for the Southeast United States and is analyzed in a GIS framework. This climatology is created using four years (2009-2012) of daily-averaged data from the NOAA high-resolution multi-sensor precipitation estimation (MPE) dataset, specifically the radar-based quantitative precipitation estimation (QPE) product and the mosaic reflectivity. The analysis associates precipitation at each pixel with the spatial scale of precipitation organization, either a mesoscale precipitation feature (MPF) or isolated storm. While the long-term averaged precipitation totals of these systems may be similar, their hydrological and climatological impacts are very different, especially at a local scale. The classification of these modes of precipitation organization in the current precipitation climatology provides information beyond standard precipitation climatologies that will benefit a range of hydrological and climatological applications. This study focuses on North Carolina and takes advantage of a GIS framework to examine hydrological responses to different modes of precipitation organization. Specifically, the following questions are addressed: First, what are the discharge response characteristics to precipitation events in different watersheds across the state, from the mountains to the coastal plain? Second, what are the different impacts on watershed discharge between MPF precipitation and isolated precipitation? We first present seasonal and annual composites of precipitation and duration of MPF and isolated storms across three regions of North Carolina: the western mountains, the central Piedmont, and the eastern coastal plain. Further analysis in a GIS framework provides information about the impacts this seasonal and geographic variability in precipitation has on watershed discharge. This analysis defines five watersheds in North Carolina based on five North Carolina river basins using ArcGIS watershed delineation

Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions

NASA Astrophysics Data System (ADS)

Safeeq, Mohammad; Fares, Ali

2011-12-01

Daily and sub-daily weather data are often required for hydrological and environmental modeling. Various weather generator programs have been used to generate synthetic climate data where observed climate data are limited. In this study, a weather data generator, ClimGen, was evaluated for generating information on daily precipitation, temperature, and wind speed at four tropical watersheds located in Hawai`i, USA. We also evaluated different daily to sub-daily weather data disaggregation methods for precipitation, air temperature, dew point temperature, and wind speed at Mākaha watershed. The hydrologic significance values of the different disaggregation methods were evaluated using Distributed Hydrology Soil Vegetation Model. MuDRain and diurnal method performed well over uniform distribution in disaggregating daily precipitation. However, the diurnal method is more consistent if accurate estimates of hourly precipitation intensities are desired. All of the air temperature disaggregation methods performed reasonably well, but goodness-of-fit statistics were slightly better for sine curve model with 2 h lag. Cosine model performed better than random model in disaggregating daily wind speed. The largest differences in annual water balance were related to wind speed followed by precipitation and dew point temperature. Simulated hourly streamflow, evapotranspiration, and groundwater recharge were less sensitive to the method of disaggregating daily air temperature. ClimGen performed well in generating the minimum and maximum temperature and wind speed. However, for precipitation, it clearly underestimated the number of extreme rainfall events with an intensity of >100 mm/day in all four locations. ClimGen was unable to replicate the distribution of observed precipitation at three locations (Honolulu, Kahului, and Hilo). ClimGen was able to reproduce the distributions of observed minimum temperature at Kahului and wind speed at Kahului and Hilo. Although the weather

Are satellite products good proxies for gauge precipitation over Singapore?

NASA Astrophysics Data System (ADS)

Hur, Jina; Raghavan, Srivatsan V.; Nguyen, Ngoc Son; Liong, Shie-Yui

2018-05-01

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000-2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate

Statistical analysis of the time and space characteristic scales for large precipitating systems in the equatorial, tropical, sahelian and mid-latitude regions.

NASA Astrophysics Data System (ADS)

Duroure, Christophe; Sy, Abdoulaye; Baray, Jean luc; Van baelen, Joel; Diop, Bouya

2017-04-01

Precipitation plays a key role in the management of sustainable water resources and flood risk analyses. Changes in rainfall will be a critical factor determining the overall impact of climate change. We propose to analyse long series (10 years) of daily precipitation at different regions. We present the Fourier densities energy spectra and morphological spectra (i.e. probability repartition functions of the duration and the horizontal scale) of large precipitating systems. Satellite data from the Global precipitation climatology project (GPCP) and local pluviometers long time series in Senegal and France are used and compared in this work. For mid-latitude and Sahelian regions (North of 12°N), the morphological spectra are close to exponential decreasing distribution. This fact allows to define two characteristic scales (duration and space extension) for the precipitating region embedded into the large meso-scale convective system (MCS). For tropical and equatorial regions (South of 12°N) the morphological spectra are close to a Levy-stable distribution (power law decrease) which does not allow to define a characteristic scale (scaling range). When the time and space characteristic scales are defined, a "statistical velocity" of precipitating MCS can be defined, and compared to observed zonal advection. Maps of the characteristic scales and Levy-stable exponent over West Africa and south Europe are presented. The 12° latitude transition between exponential and Levy-stable behaviors of precipitating MCS is compared with the result of ECMWF ERA-Interim reanalysis for the same period. This morphological sharp transition could be used to test the different parameterizations of deep convection in forecast models.

Implications of a decrease in the precipitation area for the past and the future

NASA Astrophysics Data System (ADS)

Benestad, Rasmus E.

2018-04-01

The total area with 24 hrs precipitation has shrunk by 7% between 50°S–50°N over the period 1998–2016, according to the satellite-based Tropical Rain Measurement Mission data. A decrease in the daily precipitation area is an indication of profound changes in the hydrological cycle, where the global rate of precipitation is balanced by the global rate of evaporation. This decrease was accompanied by increases in total precipitation, evaporation, and wet-day mean precipitation. If these trends are real, then they suggest increased drought frequencies and more intense rainfall. Satellite records, however, may be inhomogeneous because they are synthesised from a number of individual missions with improved technology over time. A linear dependency was also found between the global mean temperature and the 50°S–50°N daily precipitation area with a slope value of ‑17 × 106 km 2/°C. This dependency was used with climate model simulations to make future projections which suggested a continued decrease that will strengthen in the future. The precipitation area evolves differently when the precipitation is accumulated over short and long time scales, however, and there has been a slight increase in the monthly precipitation area while the daily precipitation area decreased. An increase on monthly scale may indicate more pronounced variations in the rainfall patterns due to migrating rain-producing phenomena.

Quantifying chemical weathering rates along a precipitation gradient on Basse-Terre Island, French Guadeloupe: new insight from U-series isotopes in weathering rinds

USGS Publications Warehouse

Engel, Jacqueline M.; May, Linda; Sak, Peter B.; Gaillardet, Jerome; Ren, Minghua; Engle, Mark A.; Brantley, Susan L.

2016-01-01

Inside soil and saprolite, rock fragments can form weathering clasts (alteration rinds surrounding an unweathered core) and these weathering rinds provide an excellent field system for investigating the initiation of weathering and long term weathering rates. Recently, uranium-series (U-series) disequilibria have shown great potential for determining rind formation rates and quantifying factors controlling weathering advance rates in weathering rinds. To further investigate whether the U-series isotope technique can document differences in long term weathering rates as a function of precipitation, we conducted a new weathering rind study on tropical volcanic Basse-Terre Island in the Lesser Antilles Archipelago. In this study, for the first time we characterized weathering reactions and quantified weathering advance rates in multiple weathering rinds across a steep precipitation gradient. Electron microprobe (EMP) point measurements, bulk major element contents, and U-series isotope compositions were determined in two weathering clasts from the Deshaies watershed with mean annual precipitation (MAP) = 1800 mm and temperature (MAT) = 23 °C. On these clasts, five core-rind transects were measured for locations with different curvature (high, medium, and low) of the rind-core boundary. Results reveal that during rind formation the fraction of elemental loss decreases in the order: Ca ≈ Na > K ≈ Mg > Si ≈ Al > Zr ≈ Ti ≈ Fe. Such observations are consistent with the sequence of reactions after the initiation of weathering: specifically, glass matrix and primary minerals (plagioclase, pyroxene) weather to produce Fe oxyhydroxides, gibbsite and minor kaolinite.Uranium shows addition profiles in the rind due to the infiltration of U-containing soil pore water into the rind as dissolved U phases. U is then incorporated into the rind as Fe-Al oxides precipitate. Such processes lead to significant U-series isotope disequilibria in the rinds

Temporal Clustering of Regional-Scale Extreme Precipitation Events in Southern Switzerland

NASA Astrophysics Data System (ADS)

Barton, Yannick; Giannakaki, Paraskevi; Von Waldow, Harald; Chevalier, Clément; Pfhal, Stephan; Martius, Olivia

2017-04-01

Temporal clustering of extreme precipitation events on subseasonal time scales is a form of compound extremes and is of crucial importance for the formation of large-scale flood events. Here, the temporal clustering of regional-scale extreme precipitation events in southern Switzerland is studied. These precipitation events are relevant for the flooding of lakes in southern Switzerland and northern Italy. This research determines whether temporal clustering is present and then identifies the dynamics that are responsible for the clustering. An observation-based gridded precipitation dataset of Swiss daily rainfall sums and ECMWF reanalysis datasets are used. To analyze the clustering in the precipitation time series a modified version of Ripley's K function is used. It determines the average number of extreme events in a time period, to characterize temporal clustering on subseasonal time scales and to determine the statistical significance of the clustering. Significant clustering of regional-scale precipitation extremes is found on subseasonal time scales during the fall season. Four high-impact clustering episodes are then selected and the dynamics responsible for the clustering are examined. During the four clustering episodes, all heavy precipitation events were associated with an upperlevel breaking Rossby wave over western Europe and in most cases strong diabatic processes upstream over the Atlantic played a role in the amplification of these breaking waves. Atmospheric blocking downstream over eastern Europe supported this wave breaking during two of the clustering episodes. During one of the clustering periods, several extratropical transitions of tropical cyclones in the Atlantic contributed to the formation of high-amplitude ridges over the Atlantic basin and downstream wave breaking. During another event, blocking over Alaska assisted the phase locking of the Rossby waves downstream over the Atlantic.

Contributions of natural climate changes and human activities to the trend of extreme precipitation

NASA Astrophysics Data System (ADS)

Gao, Lu; Huang, Jie; Chen, Xingwei; Chen, Ying; Liu, Meibing

2018-06-01

This study focuses on the analysis of the nonstationarity characteristics of extreme precipitation and their attributions in the southeastern coastal region of China. The maximum daily precipitation (MDP) series is extracted from observations at 79 meteorological stations in the study area during the first flood season (April-June) from 1960 to 2012. The trends of the mean (Mn) and variance (Var) of MDP are detected using the Generalized Additive Models for Location, Scale, and Shape parameters (GAMLSS) and Mann-Kendall test. The contributions of natural climate change and human activities to the Mn and Var changes of MDP are investigated using six large-scale circulation variables and emissions of four greenhouse gases based on GAMLSS and a contribution analysis method. The results demonstrate that the nonstationarity of extreme precipitation on local scales is significant. The Mn and Var of extreme precipitation increase in the north of Zhejiang, the middle of Fujian, and the south of Guangdong. In general, natural climate change contributes more to Mn from 1960 to 2012 than to Var. However, human activities cause a greater Var in the rapid socioeconomic development period (1986-2012) than in the slow socioeconomic development period (1960-1985), especially in Zhejiang and Guangdong. The community should pay more attention to the possibility of extreme precipitation events and associated disasters triggered by human activities.

Measurement of precipitation using lysimeters

NASA Astrophysics Data System (ADS)

Fank, Johann; Klammler, Gernot

2013-04-01

Austria's alpine foothill aquifers contain important drinking water resources, but are also used intensively for agricultural production. These groundwater bodies are generally recharged by infiltrating precipitation. A sustainable water resources management of these aquifers requires quantifying real evapotranspiration (ET), groundwater recharge (GR), precipitation (P) and soil water storage change (ΔS). While GR and ΔS can be directly measured by weighable lysimeters and P by separate precipitation gauges, ET is determined by solving the climatic water balance ET = P GR ± ΔS. According to WMO (2008) measurement of rainfall is strongly influenced by precipitation gauge errors. Most significant errors result from wind loss, wetting loss, evaporation loss, and due to in- and out-splashing of water. Measuring errors can be reduced by a larger area of the measuring gaugés surface and positioning the collecting vessel at ground level. Modern weighable lysimeters commonly have a surface of 1 m², are integrated into their typical surroundings of vegetation cover (to avoid oasis effects) and allow scaling the mass change of monolithic soil columns in high measuring accuracy (0.01 mm water equivalent) and high temporal resolution. Thus, also precipitation can be quantified by measuring the positive mass changes of the lysimeter. According to Meissner et al. (2007) also dew, fog and rime can be determined by means of highly precise weighable lysimeters. Furthermore, measuring precipitation using lysimeters avoid common measuring errors (WMO 2008) at point scale. Though, this method implicates external effects (background noise, influence of vegetation and wind) which affect the mass time series. While the background noise of the weighing is rather well known and can be filtered out of the mass time series, the influence of wind, which blows through the vegetation and affects measured lysimeter mass, cannot be corrected easily since there is no clear relation between

Improvements to the gridding of precipitation data across Europe under the E-OBS scheme

NASA Astrophysics Data System (ADS)

Cornes, Richard; van den Besselaar, Else; Jones, Phil; van der Schrier, Gerard; Verver, Ge

2016-04-01

Gridded precipitation data are a valuable resource for analyzing past variations and trends in the hydroclimate. Such data also provide a reference against which model simulations may be driven, compared and/or adjusted. The E-OBS precipitation dataset is widely used for such analyses across Europe, and is particularly valuable since it provides a spatially complete, daily field across the European domain. In this analysis, improvements to the E-OBS precipitation dataset will be presented that aim to provide a more reliable estimate of grid-box precipitation values, particularly in mountainous areas and in regions with a relative sparsity of input station data. The established three-stage E-OBS gridding scheme is retained, whereby monthly precipitation totals are gridded using a thin-plate spline; daily anomalies are gridded using indicator kriging; and the final dataset is produced by multiplying the two grids. The current analysis focuses on improving the monthly thin-plate spline, which has overall control on the final daily dataset. The results from different techniques are compared and the influence on the final daily data is assessed by comparing the data against gridded country-wide datasets produced by various National Meteorological Services

On the forecast of runoff based on the harmonic analysis of time series of precipitation in the catchment area

NASA Astrophysics Data System (ADS)

Cherednichenko, A. V.; Cherednichenko, A. V.; Cherednichenko, V. S.

2018-01-01

It is shown that a significant connection exists between the most important harmonics, extracted in the process of harmonic analysis of time series of precipitation in the catchment area of rivers and the amount of runoff. This allowed us to predict the size of the flow for a period of up to 20 years, assuming that the main parameters of the harmonics are preserved at the predicted time interval. The results of such a forecast for three river basins of Kazakhstan are presented.

Assessment of CLIGEN precipitation and storm pattern generation under four precipitation depth categories in China

USDA-ARS?s Scientific Manuscript database

CLIGEN (CLImate GENerator) is a widely used stochastic weather generator to simulate continuous daily precipitation and storm pattern information for hydrological and soil erosion models. Although CLIGEN has been tested in several regions in the world, thoroughly assessment before applying it to Chi...

Transient deformation from daily GPS displacement time series: postseismic deformation, ETS and evolving strain rates

NASA Astrophysics Data System (ADS)

Bock, Y.; Fang, P.; Moore, A. W.; Kedar, S.; Liu, Z.; Owen, S. E.; Glasscoe, M. T.

2016-12-01

Detection of time-dependent crustal deformation relies on the availability of accurate surface displacements, proper time series analysis to correct for secular motion, coseismic and non-tectonic instrument offsets, periodic signatures at different frequencies, and a realistic estimate of uncertainties for the parameters of interest. As part of the NASA Solid Earth Science ESDR System (SESES) project, daily displacement time series are estimated for about 2500 stations, focused on tectonic plate boundaries and having a global distribution for accessing the terrestrial reference frame. The "combined" time series are optimally estimated from independent JPL GIPSY and SIO GAMIT solutions, using a consistent set of input epoch-date coordinates and metadata. The longest time series began in 1992; more than 30% of the stations have experienced one or more of 35 major earthquakes with significant postseismic deformation. Here we present three examples of time-dependent deformation that have been detected in the SESES displacement time series. (1) Postseismic deformation is a fundamental time-dependent signal that indicates a viscoelastic response of the crust/mantle lithosphere, afterslip, or poroelastic effects at different spatial and temporal scales. It is critical to identify and estimate the extent of postseismic deformation in both space and time not only for insight into the crustal deformation and earthquake cycles and their underlying physical processes, but also to reveal other time-dependent signals. We report on our database of characterized postseismic motions using a principal component analysis to isolate different postseismic processes. (2) Starting with the SESES combined time series and applying a time-dependent Kalman filter, we examine episodic tremor and slow slip (ETS) in the Cascadia subduction zone. We report on subtle slip details, allowing investigation of the spatiotemporal relationship between slow slip transients and tremor and their

Scaling of Precipitation Extremes Modelled by Generalized Pareto Distribution

NASA Astrophysics Data System (ADS)

Rajulapati, C. R.; Mujumdar, P. P.

2017-12-01

Precipitation extremes are often modelled with data from annual maximum series or peaks over threshold series. The Generalized Pareto Distribution (GPD) is commonly used to fit the peaks over threshold series. Scaling of precipitation extremes from larger time scales to smaller time scales when the extremes are modelled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The GPD parameters and exceedance rate parameters are modelled by the Bayesian approach and the uncertainty in scaling exponent is quantified. A quantile based modification in the scaling relationship is proposed for obtaining the varying thresholds and exceedance rate parameters for shorter durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations.

Precipitation Effects on Microbial Pollution in a River: Lag Structures and Seasonal Effect Modification

PubMed Central

Tornevi, Andreas; Bergstedt, Olof; Forsberg, Bertil

2014-01-01

Background The river Göta Älv is a source of freshwater for 0.7 million swedes. The river is subject to contamination from sewer systems discharge and runoff from agricultural lands. Climate models projects an increase in precipitation and heavy rainfall in this region. This study aimed to determine how daily rainfall causes variation in indicators of pathogen loads, to increase knowledge of variations in river water quality and discuss implications for risk management. Methods Data covering 7 years of daily monitoring of river water turbidity and concentrations of E. coli, Clostridium and coliforms were obtained, and their short-term variations in relation with precipitation were analyzed with time series regression and non-linear distributed lag models. We studied how precipitation effects varied with season and compared different weather stations for predictive ability. Results Generally, the lowest raw water quality occurs 2 days after rainfall, with poor raw water quality continuing for several more days. A rainfall event of >15 mm/24-h (local 95 percentile) was associated with a three-fold higher concentration of E. coli and 30% higher turbidity levels (lag 2). Rainfall was associated with exponential increases in concentrations of indicator bacteria while the effect on turbidity attenuated with very heavy rainfall. Clear associations were also observed between consecutive days of wet weather and decreased water quality. The precipitation effect on increased levels of indicator bacteria was significant in all seasons. Conclusions Rainfall elevates microbial risks year-round in this river and freshwater source and acts as the main driver of varying water quality. Heavy rainfall appears to be a better predictor of fecal pollution than water turbidity. An increase of wet weather and extreme events with climate change will lower river water quality even more, indicating greater challenges for drinking water producers, and suggesting better control of sources of

An application of sample entropy to precipitation in Paraíba State, Brazil

NASA Astrophysics Data System (ADS)

Xavier, Sílvio Fernando Alves; da Silva Jale, Jader; Stosic, Tatijana; dos Santos, Carlos Antonio Costa; Singh, Vijay P.

2018-05-01

A climate system is characterized to be a complex non-linear system. In order to describe the complex characteristics of precipitation series in Paraíba State, Brazil, we aim the use of sample entropy, a kind of entropy-based algorithm, to evaluate the complexity of precipitation series. Sixty-nine meteorological stations are distributed over four macroregions: Zona da Mata, Agreste, Borborema, and Sertão. The results of the analysis show that intricacies of monthly average precipitation have differences in the macroregions. Sample entropy is able to reflect the dynamic change of precipitation series providing a new way to investigate complexity of hydrological series. The complexity exhibits areal variation of local water resource systems which can influence the basis for utilizing and developing resources in dry areas.

Long Term Precipitation Pattern Identification and Derivation of Non Linear Precipitation Trend in a Catchment using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, Vinayakam

2017-04-01

Precipitation is the major component in the hydrologic cycle. Awareness of not only the total amount of rainfall pertaining to a catchment, but also the pattern of its spatial and temporal distribution are equally important in the management of water resources systems in an efficient way. Trend is the long term direction of a time series; it determines the overall pattern of a time series. Singular Spectrum Analysis (SSA) is a time series analysis technique that decomposes the time series into small components (eigen triples). This property of the method of SSA has been utilized to extract the trend component of the rainfall time series. In order to derive trend from the rainfall time series, we need to select components corresponding to trend from the eigen triples. For this purpose, periodogram analysis of the eigen triples have been proposed to be coupled with SSA, in the present study. In the study, seasonal data of England and Wales Precipitation (EWP) for a time period of 1766-2013 have been analyzed and non linear trend have been derived out of the precipitation data. In order to compare the performance of SSA in deriving trend component, Mann Kendall (MK) test is also used to detect trends in EWP seasonal series and the results have been compared. The result showed that the MK test could detect the presence of positive or negative trend for a significance level, whereas the proposed methodology of SSA could extract the non-linear trend present in the rainfall series along with its shape. We will discuss further the comparison of both the methodologies along with the results in the presentation.

Precipitation composition and wet deposition temporal pattern in Central Serbia for the period from 1998 to 2004.

PubMed

Golobocanin, D; Zujić, A; Milenković, A; Miljević, N

2008-07-01

Bulk samples collected on a daily basis at three principal meteorological stations in central Serbia were analyzed on chloride (Cl(-)), nitrate (NO(3)(-)), sulfate (SO(4)(2-)), sodium (Na(+)), ammonium (NH(4)(+)), potassium (K(+)), calcium (Ca(2+)), and magnesium (Mg(2+)) in addition to precipitation amount, pH and conductivity measurements over the period 1998-2004. The data were subjected to variety of analyses (linear regression, principal component analysis, time series analysis) to characterize precipitation chemistry in the study area. The most abundant ion was SO(2-)(4) with annual volume weighted mean concentration of 242 microeq L(-1). Neutralization of precipitation acidity occurs both as a result of the dissolution of alkaline compounds containing Ca(2+), Mg(2+), and K(+) as well as the absorption of ammonia. The ratio of SO(4)(2-)/NO(3)(-) was above 5, which indicated that the combustion process of low-grade domestic lignite for electricity generation from coal-fired thermal power plants was the main source of pollution in the investigated area. A considerable mean annual bulk wet deposition of SO(4)-S determined by precipitation amount and concentrations of sulfate in the precipitation was calculated to be 12-35 kg ha(-1).

Time series study of concentrations of SO4(2-) and H+ in precipitation and soil waters in Norway.

PubMed

Kvaalen, H; Solberg, S; Clarke, N; Torp, T; Aamlid, D

2002-01-01

Along with a steady reduction of acid inputs during 14 years of intensive forest monitoring in Norway, the influence of acid deposition upon soil water acidity is gradually reduced in favour of other and internal sources of H+ and sulphate, in particular from processes in the upper soil layer. We used statistical analyses in two steps for precipitation, throughfall and soil water at 5, 15 and 40 cm depths. Firstly, we employed time series analyses to model the temporal variation as a long-term linear trend and a monthly variation, and by this filtered out residual, weekly variation. Secondly, we used the parameter estimates and the residuals from this to show that the long term, the monthly and the weekly variation in one layer were correlated to similar temporal variation in the above, adjacent layer. This was strongly evident for throughfall correlated to precipitation, but much weaker for soil water. Continued acidification in soil water on many plots suggests that the combined effects of anthropogenic and natural acid inputs exceed in places the buffering capacity of the soil.

Changes in precipitation regime in the Baltic countries in 1966-2015

NASA Astrophysics Data System (ADS)

Jaagus, Jaak; Briede, Agrita; Rimkus, Egidijus; Sepp, Mait

2018-01-01

The aim of the study was to analyse trends and regime shifts in time series of monthly, seasonal and annual precipitation in the eastern Baltic countries (Lithuania, Latvia, Estonia) during 1966-2015. Data from 54 stations with nearly homogeneous series were used. The Mann-Kendall test was used for trend analysis and the Rodionov test for the analysis of regime shifts. Rather few statistically significant trends ( p < 0.05) and regime shifts were determined. The highest increase (by approximately 10 mm per decade) was observed in winter precipitation when a significant trend was found at the large majority of stations. For monthly precipitation, increasing trends were detected at many stations in January, February and June. Weak negative trends revealed at few stations in April and September. Annual precipitation has generally increased, but the trend is mostly insignificant. The analysis of regime shifts revealed some significant abrupt changes, the most important of which were upward shifts in winter, in January and February precipitation at many stations since 1990 or in some other years (1989, 1995). A return shift in the time series of February precipitation occurred since 2003. The most significant increase in precipitation was determined in Latvia and the weakest increase in Lithuania.

Disaggregating from daily to sub-daily rainfall under a future climate

NASA Astrophysics Data System (ADS)

Westra, S.; Evans, J.; Mehrotra, R.; Sharma, A.

2012-04-01

We describe an algorithm for disaggregating daily rainfall into sub-daily rainfall 'fragments' (continuous fine-resolution rainfall sequences whose total depth sums to the daily rainfall amount) under a future, warmer climate. The basis of the algorithm is re-sample sub-daily fragments from the historical record conditional on the total daily rainfall amount and a range of atmospheric predictors representative of the future climate. The logic is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns which occurred on warmer days at the same location, or at locations which have an atmospheric profile more reflective of expected future conditions. When looking at the scaling from daily to sub-daily rainfall over the historical record, it was found that the relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rain falling over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature this effect was almost entirely eliminated, providing a basis for suggesting the approach may be valid when extrapolating sub-daily sequences to a future climate. The method of fragments algorithm was then applied to nine stations around Australia, and showed that when holding the total daily rainfall constant, the maximum intensity of a short duration (6 minute) rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, between 3.1% and 6.8% for the maximum one hour burst, and between 1.5% and 3.5% for the fraction of the day with no rainfall. This highlights that a large proportion of the change to the distribution of precipitation in the future is likely to occur at sub-daily timescales, with significant implications for many hydrological systems.

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

NASA Astrophysics Data System (ADS)

Duveiller, G.; Donatelli, M.; Fumagalli, D.; Zucchini, A.; Nelson, R.; Baruth, B.

2017-02-01

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure

The influence of atmospheric circulation types on regional patterns of precipitation in Marmara (NW Turkey)

NASA Astrophysics Data System (ADS)

Baltacı, H.; Kındap, T.; Ünal, A.; Karaca, M.

2017-02-01

In this study, regional patterns of precipitation in Marmara are described for the first time by means of Ward's hierarchical cluster analysis. Daily values of winter precipitation data based on 19 meteorological stations were used for the period from 1960 to 2012. Five clusters of coherent zones were determined, namely Black Sea-Marmara, Black Sea, Marmara, Thrace, and Aegean sub-regions. To investigate the prevailing atmospheric circulation types (CTs) that cause precipitation occurrence and intensity in these five different rainfall sub-basins, objective Lamb weather type (LWT) methodology was applied to National Centers of Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis of daily mean sea level pressure (MSLP) data. Precipitation occurrence suggested that wet CTs (i.e. N, NE, NW, and C) offer a high chance of precipitation in all sub-regions. For the eastern (western) part of the region, the high probability of rainfall occurrence is shown under the influence of E (SE, S, SW) atmospheric CTs. In terms of precipitation intensity, N and C CTs had the highest positive gradients in all the sub-basins of the Marmara. In addition, although Marmara and Black Sea sub-regions have the highest daily rainfall potential during NE types, high daily rainfall totals are recorded in all sub-regions except the Black Sea during NW types.

Variability in the microcanonical cascades parameters among gauges of urban precipitation monitoring network

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Rupp, David; Adamowski, Witold

2013-04-01

all gauges and timescales with exception of two gauges located at the city limits (one gauge was located on the Okęcie airport). We evaluated the performance of the microcanonical cascades at disaggregating 1280-min (quasi daily precipitation totals) into 5-min rainfall data for selected gauges. Synthetic time series were analyzed with respect to their intermittency and variability of rainfall intensities and compared to observational series. We showed that microcanonical cascades models could be used in practice for generating synthetic rainfall time series suitable as input to urban hydrology models in Warsaw.

Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.

NASA Astrophysics Data System (ADS)

Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.

2009-09-01

environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).

The TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-Global Precipitation Estimates at Fine Scales

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Gu, Guojun; Nelkin, Eric J.; Bowman, Kenneth P.; Stocker, Erich; Wolff, David B.

2006-01-01

The TRMM Multi-satellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining multiple precipitation estimates from satellites, as well as gauge analyses where feasible, at fine scales (0.25 degrees x 0.25 degrees and 3-hourly). It is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The data set covers the latitude band 50 degrees N-S for the period 1998 to the delayed present. Early validation results are as follows: The TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate dependent low bias due to lack of sensitivity to low precipitation rates in one of the input products (based on AMSU-B). At finer scales the TMPA is successful at approximately reproducing the surface-observation-based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other fine-scale estimators. Examples are provided of a flood event and diurnal cycle determination.

A global dataset of sub-daily rainfall indices

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Lewis, E.; Blenkinsop, S.; Guerreiro, S.; Li, X.; Barbero, R.; Chan, S.; Lenderink, G.; Westra, S.

2017-12-01

It is still uncertain how hydrological extremes will change with global warming as we do not fully understand the processes that cause extreme precipitation under current climate variability. The INTENSE project is using a novel and fully-integrated data-modelling approach to provide a step-change in our understanding of the nature and drivers of global precipitation extremes and change on societally relevant timescales, leading to improved high-resolution climate model representation of extreme rainfall processes. The INTENSE project is in conjunction with the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes' and the Global Water and Energy Exchanges Project (GEWEX) Science questions. A new global sub-daily precipitation dataset has been constructed (data collection is ongoing). Metadata for each station has been calculated, detailing record lengths, missing data, station locations. A set of global hydroclimatic indices have been produced based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. This will provide a unique global data resource on sub-daily precipitation whose derived indices will be freely available to the wider scientific community.

Relationship between convective precipitation and lightning activity using radar quantitative precipitation estimates and total lightning data

NASA Astrophysics Data System (ADS)

Pineda, N.; Rigo, T.; Bech, J.; Argemí, O.

2009-09-01

Thunderstorms can be characterized by both rainfall and lightning. The relationship between convective precipitation and lightning activity may be used as an indicator of the rainfall regime. Besides, a better knowledge of local thunderstorm phenomenology can be very useful to assess weather surveillance tasks. Two types of approach can be distinguished in the bibliography when analyzing the rainfall and lightning activity. On one hand, rain yields (ratio of rain mass to cloud-to-ground flash over a common area) calculated for long temporal and spatial domains and using rain-gauge records to estimate the amounts of precipitation. On the other hand, a case-by-case approach has been used in many studies to analyze the relationship between convective precipitation and lightning in individual storms, using weather radar data to estimate rainfall volumes. Considering a local thunderstorm case study approach, the relation between rainfall and lightning is usually quantified as the Rainfall-Lightning ratio (RLR). This ratio estimates the convective rainfall volume per lightning flash. Intense storms tend to produce lower RLR values than moderate storms, but the range of RLR found in diverse studies is quite wide. This relationship depends on thunderstorm type, local climatology, convective regime, type of lightning flashes considered, oceanic and continental storms, etc. The objective of this paper is to analyze the relationship between convective precipitation and lightning in a case-by-case approach, by means of daily radar-derived quantitative precipitation estimates (QPE) and total lightning data, obtained from observations of the Servei Meteorològic de Catalunya remote sensing systems, which covers an area of approximately 50000 km2 in the NE of the Iberian Peninsula. The analyzed dataset is composed by 45 thunderstorm days from April to October 2008. A good daily correlation has been found between the radar QPE and the CG flash counts (best linear fit with a R^2

The Potential of Tropospheric Gradients for Regional Precipitation Prediction

NASA Astrophysics Data System (ADS)

Boisits, Janina; Möller, Gregor; Wittmann, Christoph; Weber, Robert

2017-04-01

Changes of temperature and humidity in the neutral atmosphere cause variations in tropospheric path delays and tropospheric gradients. By estimating zenith wet delays (ZWD) and gradients using a GNSS reference station network the obtained time series provide information about spatial and temporal variations of water vapour in the atmosphere. Thus, GNSS-based tropospheric parameters can contribute to the forecast of regional precipitation events. In a recently finalized master thesis at TU Wien the potential of tropospheric gradients for weather prediction was investigated. Therefore, ZWD and gradient time series at selected GNSS reference stations were compared to precipitation data over a period of six months (April to September 2014). The selected GNSS stations form two test areas within Austria. All required meteorological data was provided by the Central Institution for Meteorology and Geodynamics (ZAMG). Two characteristics in ZWD and gradient time series can be anticipated in case of an approaching weather front. First, an induced asymmetry in tropospheric delays results in both, an increased magnitude of the gradient and in gradients pointing towards the weather front. Second, an increase in ZWD reflects the increased water vapour concentration right before a precipitation event. To investigate these characteristics exemplary test events were processed. On the one hand, the sequence of the anticipated increase in ZWD at each GNSS station obtained by cross correlation of the time series indicates the direction of the approaching weather front. On the other hand, the corresponding peak in gradient time series allows the deduction of the direction of movement as well. To verify the results precipitation data from ZAMG was used. It can be deduced, that tropospheric gradients show high potential for predicting precipitation events. While ZWD time series rather indicate the orientation of the air mass boundary, gradients rather indicate the direction of movement

Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin, India

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj; Gautam, Amar Kant; Palmate, Santosh S.; Pandey, Ashish; Suryavanshi, Shakti; Rathore, Neha; Sharma, Nayan

2017-08-01

To support the GPM mission which is homologous to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), this study has been undertaken to evaluate the accuracy of Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TMPA) daily-accumulated precipitation products for 5 years (2008-2012) using the statistical methods and contingency table method. The analysis was performed on daily, monthly, seasonal and yearly basis. The TMPA precipitation estimates were also evaluated for each grid point i.e. 0.25° × 0.25° and for 18 rain gauge stations of the Betwa River basin, India. Results indicated that TMPA precipitation overestimates the daily and monthly precipitation in general, particularly for the middle sub-basin in the non-monsoon season. Furthermore, precision of TMPA precipitation estimates declines with the decrease of altitude at both grid and sub-basin scale. The study also revealed that TMPA precipitation estimates provide better accuracy in the upstream of the basin compared to downstream basin. Nevertheless, the detection capability of daily TMPA precipitation improves with increase in altitude for drizzle rain events. However, the detection capability decreases during non-monsoon and monsoon seasons when capturing moderate and heavy rain events, respectively. The veracity of TMPA precipitation estimates was improved during the rainy season than during the dry season at all scenarios investigated. The analyses suggest that there is a need for better precipitation estimation algorithm and extensive accuracy verification against terrestrial precipitation measurement to capture the different types of rain events more reliably over the sub-humid tropical regions of India.

Precipitation extremes in the Iberian Peninsula: an overview of the CLIPE project

NASA Astrophysics Data System (ADS)

Santos, João A.; Gonçalves, Paulo M.; Rodrigues, Tiago; Carvalho, Maria J.; Rocha, Alfredo

2014-05-01

The main aims of the project "Climate change of precipitation extreme episodes in the Iberian Peninsula and its forcing mechanisms - CLIPE" are 1) to diagnose the climate change signal in the precipitation extremes over the Iberian Peninsula (IP) and 2) to identify the underlying physical mechanisms. For the first purpose, a multi-model ensemble of 25 Regional Climate Model (RCM) simulations, from the ENSEMBLES project, is used. These experiments were generated by 15 RCMs, driven by five General Circulation Models (GCMs) under both historic conditions (1951-2000) and SRES A1B scenario (2001-2100). In this project, daily precipitation and mean sea level pressure, for the periods 1961-1990 (recent past) and 2021-2100 (future), are used. Using the Standardised Precipitation Index (SPI) on a daily basis, a precipitation extreme is defined by the pair of threshold values (Dmin, Imin), where Dmin is the minimum number of consecutive days with daily SPI above the Imin value. For both past and future climates, a precipitation extreme of a specific type is then characterised by two variables: the number of episodes with a specific duration in days and the number of episodes with a specific mean intensity (SPI/duration). Climate change is also assessed by changes in their Probability Density Functions (PDFs), estimated at sectors representative of different precipitation regimes. Lastly, for the second objective of this project, links between precipitation and Circulation Weather Regimes (CWRs) are explored for both past and future climates. Acknowledgments: this work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project CLIPE (PTDC/AAC-CLI/111733/2009).

Monitoring Precipitation from Space: targeting Hydrology Community?

NASA Astrophysics Data System (ADS)

Hong, Y.; Turk, J.

2005-12-01

During the past decades, advances in space, sensor and computer technology have made it possible to estimate precipitation nearly globally from a variety of observations in a relatively direct manner. The success of Tropical Precipitation Measuring Mission (TRMM) has been a significant advance for modern precipitation estimation algorithms to move toward daily quarter degree measurements, while the need for precipitation data at temporal-spatial resolutions compatible with hydrologic modeling has been emphasized by the end user: hydrology community. Can the future deployment of Global Precipitation Measurement constellation of low-altitude orbiting satellites (covering 90% of the global with a sampling interval of less than 3-hours), in conjunction with the existing suite of geostationary satellites, results in significant improvements in scale and accuracy of precipitation estimates suitable for hydrology applications? This presentation will review the current state of satellite-derived precipitation estimation and demonstrate the early results and primary barriers to full global high-resolution precipitation coverage. An attempt to facilitate the communication between data producers and users will be discussed by developing an 'end-to-end' uncertainty propagation analysis framework to quantify both the precipitation estimation error structure and the error influence on hydrological modeling.

Evaluation from 3-Years Time Serie of Daily Actual Evapotranspiration over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Faivre, R.; Menenti, M.

2016-08-01

The estimation of turbulent uxes is of primary interest for hydrological and climatological studies. Also the use of optical remote sensing data in the VNIR and TIR domain already proved to allow for the parameterization of surface energy balance, leading to many algorithms. Their use over arid high elevation areas require detailed characterisation of key surface physical properties and atmospheric statement at a reference level. Satellite products aquired over the Tibetan Plateau and simulations results delivered in the frame of the CEOP-AEGIS project provide incentives for a regular analysis at medium scale.This work aims at evaluating the use Feng-Yun 2 series and MODIS data (VNIR and TIR) for land surface evapotranspiration (ET) daily mapping based on SEBI algorithm, over the whole Tibetan Plateau (Faivre, 2014). An evaluation is performed over some reference sites set-up through the Tibetan Plateau.

An Innovative Metric to Evaluate Satellite Precipitation's Spatial Distribution

NASA Astrophysics Data System (ADS)

Liu, H.; Chu, W.; Gao, X.; Sorooshian, S.

2011-12-01

precipitation product, CMORPH, against the U.S. daily precipitation analysis of Climate Prediction Center (CPC) at a daily and .25o scale over the Western U.S.

An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland

NASA Astrophysics Data System (ADS)

Coll, John; Curley, Mary; Domonkos, Peter; Aguilar, Enric; Walsh, Seamus; Sweeney, John

2015-04-01

Climate change studies based only on raw long-term data are potentially flawed due to the many breaks introduced from non-climatic sources. Consequently, accurate climate data is an essential prerequisite for basing climate related decision making on; and quality controlled, homogenised climate data are becoming integral to European Union Member State efforts to deliver climate services. Ireland has a good repository of monthly precipitation data at approximately 1900 locations stored in the Met Éireann database. The record length at individual precipitation stations varies greatly. However, an audit of the data established the continuous record length at each station and the number of missing months, and based on this two initial subsets of station series (n = 88 and n = 110) were identified for preliminary homogenisation efforts. The HOMER joint detection algorithm was applied to the combined network of these 198 longer station series on an Ireland-wide basis where contiguous intact monthly records ranged from ~40 to 71 years (1941 - 2010). HOMER detected 91 breaks in total in the country-wide series analysis distributed across 63 (~32%) of the 71 year series records analysed. In a separate approach, four sub-series clusters (n = 38 - 61) for the 1950 - 2010 period were used in a parallel analysis applying both ACMANT and HOMER to a regionalised split of the 198 series. By comparison ACMANT detected a considerably higher number of breaks across the four regional series clusters, 238 distributed across 123 (~62%) of the 61 year series records analysed. These preliminary results indicate a relatively high proportion of detected breaks in the series, a situation not generally reflected in observed later 20th century precipitation records across Europe (Domonkos, 2014). However, this elevated ratio of series with detected breaks (~32% in HOMER and ~62% in ACMANT) parallels the break detection rate in a recent analysis of series in the Netherlands (Buishand et al

Observations and simulations of the interactions between clouds, radiation, and precipitation

NASA Astrophysics Data System (ADS)

Naegele, Alexandra Claire

Increasing precipitation and warming temperatures associated with climate change have been documented across the globe, including in the Northeast US. These climate changes threaten human health in many ways. Research is necessary to understand and explain the relationship between climate change and human health. Extreme weather events such as extreme temperatures, convective storms, floods, lightning events, wintry precipitation, and low visibility, are frequently associated with adverse effects on human health. While more media attention is typically given to events that cause the most structural or economic damage (e.g., tornadoes, hurricanes, earthquakes, etc.), extreme temperatures ultimately account for the greatest loss of life in the US. Extreme weather events can be unpredictable; however, improved knowledge and technology allow meteorologists to accurately forecast many of these events, specifically extreme temperature and precipitation events. Advancing our knowledge of climate variability and trends in extreme weather can inform: public education programs to alert the community of the dangers of extreme heat or cold, emergency response plans to hazardous weather conditions, and current thresholds for emergency alerts. This study evaluates trends in extreme weather events across New Hampshire and links these extreme events to adverse health outcomes. Using data from NCEI Global Historical Climatological Network (GHCN) - Daily dataset (1981 - 2015), five daily xiii Extreme Weather Metrics (EWMs) were defined: Daily Maximum Temperature ≤32°F, Daily Maximum Temperature ≥90°F, Daily Maximum Temperature ≥95°F, Daily Precipitation ≥1", and Daily Precipitation ≥2". Relevant human health outcomes were extracted from the New Hampshire Hospital Discharge Dataset for the years 2001-2009. Health cases were defined based on the International Classification of Disease 9th Revision (ICD-9). Outcomes in this analysis include: All-Cause Injury, Vehicle

A precipitation-runoff model for simulating natural streamflow conditions in the Smith River watershed, Montana, water years 1996-2008

USGS Publications Warehouse

Chase, Katherine J.; Caldwell, Rodney R.; Stanley, Andrea K.

2014-01-01

This report documents the construction of a precipitation-runoff model for simulating natural streamflow in the Smith River watershed, Montana. This Precipitation-Runoff Modeling System model, constructed in cooperation with the Meagher County Conservation District, can be used to examine the general hydrologic framework of the Smith River watershed, including quantification of precipitation, evapotranspiration, and streamflow; partitioning of streamflow between surface runoff and subsurface flow; and quantifying contributions to streamflow from several parts of the watershed. The model was constructed by using spatial datasets describing watershed topography, the streams, and the hydrologic characteristics of the basin soils and vegetation. Time-series data (daily total precipitation, and daily minimum and maximum temperature) were input to the model to simulate daily streamflow. The model was calibrated for water years 2002–2007 and evaluated for water years 1996–2001. Though water year 2008 was included in the study period to evaluate water-budget components, calibration and evaluation data were unavailable for that year. During the calibration and evaluation periods, simulated-natural flow values were compared to reconstructed-natural streamflow data. These reconstructed-natural streamflow data were calculated by adding Bureau of Reclamation’s depletions data to the observed streamflows. Reconstructed-natural streamflows represent estimates of streamflows for water years 1996–2007 assuming there was no agricultural water-resources development in the watershed. Additional calibration targets were basin mean monthly solar radiation and potential evapotranspiration. The model estimated the hydrologic processes in the Smith River watershed during the calibration and evaluation periods. Simulated-natural mean annual and mean monthly flows generally were the same or higher than the reconstructed-natural streamflow values during the calibration period, whereas

BOREAS HYD-8 Gross Precipitation Data

NASA Technical Reports Server (NTRS)

Fernandes, Richard; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-08 team made measurements of surface hydrological processes at the Southern Study Area-Old Black Spruce (SSA-OBS) Tower Flux site to support its research into point hydrological processes and the spatial variation of these processes. Data collected may be useful in characterizing canopy interception, drip, throughfall, moss interception, drainage, evaporation, and capacity during the growing season at daily temporal resolution. This particular data set contains the gross precipitation measurements for July to August 1996. Gross precipitation is the precipitation that falls that is not intercepted by tree canopies. These data are stored in ASCII text files. The HYD-08 gross precipitation data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

An analysis of the daily precipitation variability in the Himalayan orogen using a statistical parameterisation and its potential in driving landscape evolution models with stochastic climatic forcing

NASA Astrophysics Data System (ADS)

Deal, Eric; Braun, Jean

2015-04-01

A current challenge in landscape evolution modelling is to integrate realistic precipitation patterns and behaviour into longterm fluvial erosion models. The effect of precipitation on fluvial erosion can be subtle as well as nonlinear, implying that changes in climate (e.g. precipitation magnitude or storminess) may have unexpected outcomes in terms of erosion rates. For example Tucker and Bras (2000) show theoretically that changes in the variability of precipitation (storminess) alone can influence erosion rate across a landscape. To complicate the situation further, topography, ultimately driven by tectonic uplift but shaped by erosion, has a major influence on the distribution and style of precipitation. Therefore, in order to untangle the coupling between climate, erosion and tectonics in an actively uplifting orogen where fluvial erosion is dominant it is important to understand how the 'rain dial' used in a landscape evolution model (LEM) corresponds to real precipitation patterns. One issue with the parameterisation of rainfall for use in an LEM is the difference between the timescales for precipitation (≤ 1 year) and landscape evolution (> 103 years). As a result, precipitation patterns must be upscaled before being integrated into a model. The relevant question then becomes: What is the most appropriate measure of precipitation on a millennial timescale? Previous work (Tucker and Bras, 2000; Lague, 2005) has shown that precipitation can be properly upscaled by taking into account its variable nature, along with its average magnitude. This captures the relative size and frequency of extreme events, ensuring a more accurate characterisation of the integrated effects of precipitation on erosion over long periods of time. In light of this work, we present a statistical parameterisation that accurately models the mean and daily variability of ground based (APHRODITE) and remotely sensed (TRMM) precipitation data in the Himalayan orogen with only a few

Precipitation and streamflow data from the Fort Carson Military Reservation and precipitation, streamflow, and suspended-sediment data from the Piñon Canyon Maneuver Site, Southeastern Colorado, 2008-2012

USGS Publications Warehouse

Brown, Christopher R.

2014-01-01

In 2013, the U.S. Geological Survey (USGS), in cooperation with the U. S. Department of the Army, compiled available precipitation and streamflow data for the years of 2008–2012 from the Fort Carson Military Reservation (Fort Carson) near Colorado Springs, Colo., and precipitation, streamflow, and suspended-sediment loads from the Piñon Canyon Maneuver Site (PCMS) near Trinidad, Colo. Graphical representations of the data presented herein are a continuation of work completed by the USGS in 2008 to gain a better understanding of spatial and temporal trends within the hydrologic data. Precipitation stations at Fort Carson and the PCMS were divided into groups based on their land-surface altitude (LSA) to determine if there is a spatial difference in precipitation amounts based on LSA for either military facility. Two-sample t-tests and Wilcoxon rank-sum tests indicated statistically significant differences exist between precipitation values at different groups for Fort Carson but not for the PCMS. All five precipitation stations at Fort Carson exhibit a decrease in median daily total precipitation from years 2002–2007 to 2008–2012. For the PCMS, median precipitation values decreased from the first study period to the second for the 13 stations monitored year-round except for Burson and Big Hills. Mean streamflow for 2008–2012 is less than mean streamflow for 1983–2007 for all stream-gaging stations at Fort Carson and at the PCMS. During the study period, each of the stream-gaging stations within the tributary channels at the PCMS accounted for less than three percent of the total streamflow at the Purgatoire River at Rock Crossing gage. Peak streamflow for 2008–2012 is less than peak streamflow for 2002–2007 at both Fort Carson and the PCMS. At the PCMS, mean suspended-sediment yield for 2008–2012 increased by 54 percent in comparison to the mean yield for 2002–2007. This increase is likely related to the destruction of groundcover by a series of

Regionalizing nonparametric models of precipitation amounts on different temporal scales

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András

2017-05-01

Parametric distribution functions are commonly used to model precipitation amounts corresponding to different durations. The precipitation amounts themselves are crucial for stochastic rainfall generators and weather generators. Nonparametric kernel density estimates (KDEs) offer a more flexible way to model precipitation amounts. As already stated in their name, these models do not exhibit parameters that can be easily regionalized to run rainfall generators at ungauged locations as well as at gauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate it for different temporal resolutions ranging from hourly to monthly. During the evaluation, the nonparametric methods are compared to commonly used parametric models like the two-parameter gamma and the mixed-exponential distribution. As water volume is considered to be an essential parameter for applications like flood modeling, a Lorenz-curve-based criterion is also introduced. To add value to the estimation of data at sub-daily resolutions, we incorporated the plentiful daily measurements in the interpolation scheme, and this idea was evaluated. The study region is the federal state of Baden-Württemberg in the southwest of Germany with more than 500 rain gauges. The validation results show that the newly proposed nonparametric interpolation scheme provides reasonable results and that the incorporation of daily values in the regionalization of sub-daily models is very beneficial.

A multi-site stochastic weather generator of daily precipitation and temperature

USDA-ARS?s Scientific Manuscript database

Stochastic weather generators are used to generate time series of climate variables that have statistical properties similar to those of observed data. Most stochastic weather generators work for a single site, and can only generate climate data at a single point, or independent time series at sever...

Multifractal analysis of the time series of daily means of wind speed in complex regions

NASA Astrophysics Data System (ADS)

Laib, Mohamed; Golay, Jean; Telesca, Luciano; Kanevski, Mikhail

2018-04-01

In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the highly varying topography of Switzerland. Our findings point out to a persistent behaviour of all the measured wind speed series (indicated by a Hurst exponent significantly larger than 0.5), and to a high multifractality degree indicating a relative dominance of the large fluctuations in the dynamics of wind speed, especially in the Swiss plateau, which is comprised between the Jura and Alp mountain ranges. The study represents a contribution to the understanding of the dynamical mechanisms of wind speed variability in mountainous regions.

Long term statistics (1845-2014) of daily runoff maxima, monthly rainfall and runoff in the Adda basin (Italian Alps) under natural and anthropogenic changes.

NASA Astrophysics Data System (ADS)

Ranzi, Roberto; Goatelli, Federica; Castioni, Camilla; Tomirotti, Massimo; Crespi, Alice; Mattea, Enrico; Brunetti, Michele; Maugeri, Maurizio

2017-04-01

A new time series of daily runoff reconstructed at the inflow in the Como Lake in the Italian Alps is presented. The time series covers a 170 years time period and includes the two largest floods ever recorded for the region: the 1868 and 1987 ones. Statistics of annual maxima show a decrease which is not statistically significant and a decrease of annual runoff which is statistically significant, instead. To investigate the possible reasons of such changes monthly temperature and precipitation are analysed. Decrease of runoff peaks can be justified by the increase of reservoir storage volumes. Evapotranspiration indexes based on monthly temperature indicate an increase of evapotranspiration losses as a possible cause of runoff decrease. Secular precipitation series for the Adda basin are then computed by a methodology projecting observational data onto a high-resolution grid (30-arc-second, DEM GTOPO30). It is based on the assumption that the spatio-temporal behaviour of a meteorological variable over a given area can be described by superimposing two fields: the climatological normals over a reference period, i.e. the climatologies, and the departure from them, i.e. the anomalies. The two fields can be reconstructed independently and are based on different datasets. To compute the precipitation climatologies all the available stations within the Adda basin are considered while, for the anomalies, only the longest and the most homogeneous records are selected. To this aim, a great effort was made to extend these series to the past as much as possible, also by digitising the historical records available from the hardcopy archives. The climatological values at each DEM cell of the Adda basin are obtained by a local weighted linear regression of precipitation versus elevation (LWLR) taking into account the closest stations with similar geographical characteristics to those of the cell itself. The anomaly field is obtained by a weighted average of the anomalies of

Data Rescue for precipitation station network in Slovak Republic

NASA Astrophysics Data System (ADS)

Fasko, Pavel; Bochníček, Oliver; Švec, Marek; Paľušová, Zuzana; Markovič, Ladislav

2016-04-01

Transparency of archive catalogues presents very important task for the data saving. It helps to the further activities e.g. digitalization and homogenization. For the time being visualization of time series continuation in precipitation stations (approximately 1250 stations) is under way in Slovak Republic since the beginning of observation (meteorological stations gradually began to operate during the second half of the 19th century in Slovakia). Visualization is joined with the activities like verification and accessibility of the data mentioned in the archive catalogue, station localization according to the historical annual books, conversion of coordinates into x-JTSK, y-JTSK and hydrological catchment assignment. Clustering of precipitation stations at the specific hydrological catchment in the map and visualization of the data duration (line graph) will lead to the effective assignment of corresponding precipitation stations for the prolongation of time series. This process should be followed by the process of turn or trend detection and homogenization. The risks and problems at verification of records from archive catalogues, their digitalization, repairs and the way of visualization will be seen in poster. During the searching process of the historical and often short time series, we realized the importance of mainly those stations, located in the middle and higher altitudes. They might be used as replacement for up to now quoted fictive points used at the construction of precipitation maps. Supplementing and enhancing the time series of individual stations will enable to follow changes in precipitation totals during the certain period as well as area totals for individual catchments in various time periods appreciated mainly by hydrologists and agro-climatologists.

Potential sources of precipitation in Lake Baikal basin

NASA Astrophysics Data System (ADS)

Shukurov, K. A.; Mokhov, I. I.

2017-11-01

Based on the data of long-term measurements at 23 meteorological stations in the Russian part of the Lake Baikal basin the probabilities of daily precipitation with different intensity and their contribution to the total precipitation are estimated. Using the trajectory model HYSPLIT_4 for each meteorological station for the period 1948-2016 the 10-day backward trajectories of air parcels, the height of these trajectories and distribution of specific humidity along the trajectories are calculated. The average field of power of potential sources of daily precipitation (less than 10 mm) for all meteorological stations in the Russian part of the Lake Baikal basin was obtained using the CWT (concentration weighted trajectory) method. The areas have been identified from which within 10 days water vapor can be transported to the Lake Baikal basin, as well as regions of the most and least powerful potential sources. The fields of the mean height of air parcels trajectories and the mean specific humidity along the trajectories are compared with the field of mean power of potential sources.

Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan

NASA Astrophysics Data System (ADS)

Chen, Po-Chun; Wang, Yuan-Heng; You, Gene Jiing-Yun; Wei, Chih-Chiang

2017-02-01

Future climatic conditions likely will not satisfy stationarity assumption. To address this concern, this study applied three methods to analyze non-stationarity in hydrologic conditions. Based on the principle of identifying distribution and trends (IDT) with time-varying moments, we employed the parametric weighted least squares (WLS) estimation in conjunction with the non-parametric discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD). Our aim was to evaluate the applicability of non-parameter approaches, compared with traditional parameter-based methods. In contrast to most previous studies, which analyzed the non-stationarity of first moments, we incorporated second-moment analysis. Through the estimation of long-term risk, we were able to examine the behavior of return periods under two different definitions: the reciprocal of the exceedance probability of occurrence and the expected recurrence time. The proposed framework represents an improvement over stationary frequency analysis for the design of hydraulic systems. A case study was performed using precipitation data from major climate stations in Taiwan to evaluate the non-stationarity of annual maximum daily precipitation. The results demonstrate the applicability of these three methods in the identification of non-stationarity. For most cases, no significant differences were observed with regard to the trends identified using WLS, DWT, and EEMD. According to the results, a linear model should be able to capture time-variance in either the first or second moment while parabolic trends should be used with caution due to their characteristic rapid increases. It is also observed that local variations in precipitation tend to be overemphasized by DWT and EEMD. The two definitions provided for the concept of return period allows for ambiguous interpretation. With the consideration of non-stationarity, the return period is relatively small under the definition of expected

The coincidence of daily rainfall events in Liberia, Costa Rica and tropical cyclones in the Caribbean basin

NASA Astrophysics Data System (ADS)

Waylen, Peter R.; Harrison, Michael

2005-10-01

The occurrence of tropical cyclones in the Caribbean and North Atlantic basins has been previously noted to have a significant effect both upon individual hydro-climatological events as well as on the quantity of annual precipitation experienced along the Pacific flank of Central America. A methodology for examining the so-called indirect effects of tropical cyclones (i.e. those effects resulting from a tropical cyclone at a considerable distance from the area of interest) on a daily rainfall record is established, which uses a variant of contingency table analysis. The method is tested using a single station on the Pacific slope of Costa Rica. Employing daily precipitation records from Liberia, north-western Costa Rica (1964-1995), and historic storm tracks of tropical cyclones in the North Atlantic, it is determined that precipitation falling in coincidence with the passage of tropical depressions, tropical storms, and hurricanes accounts for approximately 15% of average annual precipitation. The greatest effects are associated with storms passing within 1300 km of the precipitation station, and are most apparent in the increased frequency of daily rainfall totals in the range of 40-60 mm, rather than in the largest daily totals. The complexity and nonstationarity of factors affecting precipitation in this region are reflected in the decline in the number of tropical cyclones and their significance to annual precipitation totals after 1980, simultaneous to an increase in annual precipitation totals. The methodology employed in this study is shown to be a useful tool in illuminating the indirect effects of tropical cyclones in the region, with the potential for application in other areas.

Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

NASA Astrophysics Data System (ADS)

Yoo, Cheolhee; Im, Jungho; Park, Seonyoung; Quackenbush, Lindi J.

2018-03-01

Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with Tmax/Tmin for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R2 of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for Tmax and Tmin in Los Angeles, and R2 of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for Tmax and Tmin in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that Tmax was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while Tmin showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.

Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme

A Global Precipitation Perspective on Persistent Extratropical Flow Anomalies

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.

1999-01-01

Two globally-complete, observation-only precipitation datasets have recently been developed for the Global Precipitation Climatology Project (GPCP). Both depend heavily on a variety of satellite input, as well as gauge data over land. The first, Version 2 x 79, provides monthly estimates on a 2.5 deg x 2.5 deg lat/long grid for the period 1979 through late 1999 (by the time of the conference). The second, the One-Degree Daily (1DD), provides daily estimates on a 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). Both are in beta test preparatory to release as official GPCP products. These datasets provide a unique perspective on the hydrological effects of the various atmospheric flow anomalies that have been identified by meteorologists. In this paper we discuss the regional precipitation effects that result from persistent extratropical flow anomalies. We will focus on the Pacific-North America (PNA) and North Atlantic Oscillation (NAO) patterns. Each characteristically becomes established on synoptic time scales, but then persists for periods that can exceed a month. The onset phase of each appears to have systematic mobile features, while the mature phase tend to be more stationary. Accordingly, composites of monthly data for outstanding positive and negative events (separately) contained in the 20-year record reveal the climatological structure of the precipitation during the mature phase. The climatological anomalies of the positive, negative, and (positive-negative) composites show the expected storm-track-related shifts in precipitation, and provide the advantage of putting the known precipitation effects over land in the context of the total pattern over land and ocean. As well, this global perspective points out some unexpected areas of correlation. Day-by-day composites of daily data anchored to the onset date demonstrate the systematic features during the onset. Although the 1DD has a fairly short record, some

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Goldberg, Richard; Chryssanthacopoulos, James

2014-01-01

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Scaling Linguistic Characterization of Precipitation Variability

NASA Astrophysics Data System (ADS)

Primo, C.; Gutierrez, J. M.

2003-04-01

Rainfall variability is influenced by changes in the aggregation of daily rainfall. This problem is of great importance for hydrological, agricultural and ecological applications. Rainfall averages, or accumulations, are widely used as standard climatic parameters. However different aggregation schemes may lead to the same average or accumulated values. In this paper we present a fractal method to characterize different aggregation schemes. The method provides scaling exponents characterizing weekly or monthly rainfall patterns for a given station. To this aim, we establish an analogy with linguistic analysis, considering precipitation as a discrete variable (e.g., rain, no rain). Each weekly, or monthly, symbolic precipitation sequence of observed precipitation is then considered as a "word" (in this case, a binary word) which defines a specific weekly rainfall pattern. Thus, each site defines a "language" characterized by the words observed in that site during a period representative of the climatology. Then, the more variable the observed weekly precipitation sequences, the more complex the obtained language. To characterize these languages, we first applied the Zipf's method obtaining scaling histograms of rank ordered frequencies. However, to obtain significant exponents, the scaling must be maintained some orders of magnitude, requiring long sequences of daily precipitation which are not available at particular stations. Thus this analysis is not suitable for applications involving particular stations (such as regionalization). Then, we introduce an alternative fractal method applicable to data from local stations. The so-called Chaos-Game method uses Iterated Function Systems (IFS) for graphically representing rainfall languages, in a way that complex languages define complex graphical patterns. The box-counting dimension and the entropy of the resulting patterns are used as linguistic parameters to quantitatively characterize the complexity of the patterns

Multi-scale fluctuation analysis of precipitation in Beijing by Extreme-point Symmetric Mode Decomposition

NASA Astrophysics Data System (ADS)

Li, Jiqing; Duan, Zhipeng; Huang, Jing

2018-06-01

With the aggravation of the global climate change, the shortage of water resources in China is becoming more and more serious. Using reasonable methods to study changes in precipitation is very important for planning and management of water resources. Based on the time series of precipitation in Beijing from 1951 to 2015, the multi-scale features of precipitation are analyzed by the Extreme-point Symmetric Mode Decomposition (ESMD) method to forecast the precipitation shift. The results show that the precipitation series have periodic changes of 2.6, 4.3, 14 and 21.7 years, and the variance contribution rate of each modal component shows that the inter-annual variation dominates the precipitation in Beijing. It is predicted that precipitation in Beijing will continue to decrease in the near future.

Evaluation of ERA-Interim precipitation data in complex terrain

NASA Astrophysics Data System (ADS)

Gao, Lu; Bernhardt, Matthias; Schulz, Karsten

2013-04-01

Precipitation controls a large variety of environmental processes, which is an essential input parameter for land surface models e.g. in hydrology, ecology and climatology. However, rain gauge networks provides the necessary information, are commonly sparse in complex terrains, especially in high mountainous regions. Reanalysis products (e.g. ERA-40 and NCEP-NCAR) as surrogate data are increasing applied in the past years. Although they are improving forward, previous studies showed that these products should be objectively evaluated due to their various uncertainties. In this study, we evaluated the precipitation data from ERA-Interim, which is a latest reanalysis product developed by ECMWF. ERA-Interim daily total precipitation are compared with high resolution gridded observation dataset (E-OBS) at 0.25°×0.25° grids for the period 1979-2010 over central Alps (45.5-48°N, 6.25-11.5°E). Wet or dry day is defined using different threshold values (0.5mm, 1mm, 5mm, 10mm and 20mm). The correspondence ratio (CR) is applied for frequency comparison, which is the ratio of days when precipitation occurs in both ERA-Interim and E-OBS dataset. The result shows that ERA-Interim captures precipitation occurrence very well with a range of CR from 0.80 to 0.97 for 0.5mm to 20mm thresholds. However, the bias of intensity increases with rising thresholds. Mean absolute error (MAE) varies between 4.5 mm day-1 and 9.5 mm day-1 in wet days for whole area. In term of mean annual cycle, ERA-Interim almost has the same standard deviation of the interannual variability of daily precipitation with E-OBS, 1.0 mm day-1. Significant wet biases happened in ERA-Interim throughout warm season (May to August) and dry biases in cold season (November to February). The spatial distribution of mean annual daily precipitation shows that ERA-Interim significant underestimates precipitation intensity in high mountains and northern flank of Alpine chain from November to March while pronounced

The use of normalized climatological anomalies to rank precipitation events in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Ramos, Alexandre M.; Trigo, Ricardo M.; Liberato, Margarida L. R.

2013-04-01

Extreme precipitation events in the Iberian Peninsula during winter months have major socio-economic impacts such as flooding, landslides, extensive property damage and life losses, and are usually associated to deep low pressure systems with Atlantic origin, although some extreme events in summer/autumn months are fed by the Mediterranean. Quite often these events are evaluated on a casuistic base and with relatively few stations. An objective method for ranking daily precipitation events is presented based on the extensive use of the most comprehensive database of daily precipitation available for the Iberian Peninsula (IB02) and spanning from 1950 to 2003, with a resolution of 0.2° (approximately 16 x 22 km at latitude 40°N), for a total of 1673 pixels. This database is based on a dense network of rain gauges, combining two national data sets, 'Spain02' for peninsular Spain and Balearic islands (Herrera et al., 2012), and 'PT02' for mainland Portugal (Belo-Pereira et al., 2011), with a total of more than two thousand stations over Spain and four hundred stations over Portugal, all quality-controlled and homogenized. The daily precipitation data from 1950 to 2003 are compared with a 30-year (1961-90) precipitation climatology to achieve a daily normalized departure from the climatology. The magnitude of an event is given daily by an index that is obtained after multiplying 1) the area (in percentage) that has precipitation anomalies above two standard deviations by 2) the mean values of these anomalies over this area. With this criterion we are able to evaluate not only the spatial extent of the precipitation events but also their spatially integrated intensity. In addition, to stress out the hydrological responses to precipitation, rankings taking into account the sum of the normalized anomalies over different time periods (3 days, 5 days and 10 days) were also computed. Here different precipitation rankings will be presented considering the entire Iberian

Scaling and clustering effects of extreme precipitation distributions

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Zhou, Yu; Singh, Vijay P.; Li, Jianfeng

2012-08-01

SummaryOne of the impacts of climate change and human activities on the hydrological cycle is the change in the precipitation structure. Closely related to the precipitation structure are two characteristics: the volume (m) of wet periods (WPs) and the time interval between WPs or waiting time (t). Using daily precipitation data for a period of 1960-2005 from 590 rain gauge stations in China, these two characteristics are analyzed, involving scaling and clustering of precipitation episodes. Our findings indicate that m and t follow similar probability distribution curves, implying that precipitation processes are controlled by similar underlying thermo-dynamics. Analysis of conditional probability distributions shows a significant dependence of m and t on their previous values of similar volumes, and the dependence tends to be stronger when m is larger or t is longer. It indicates that a higher probability can be expected when high-intensity precipitation is followed by precipitation episodes with similar precipitation intensity and longer waiting time between WPs is followed by the waiting time of similar duration. This result indicates the clustering of extreme precipitation episodes and severe droughts or floods are apt to occur in groups.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2014-10-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

Frequency of urban building fires as related to daily weather conditions

Treesearch

Arthur R. Pirsko; Wallace L. Fons

1956-01-01

Daily weather elements of precipitation, wind, mean temperature, relative humidity, and dew-point temperature for selected urban areas (approximately 850,000 population) in the United States are statistically analyzed to determine their correlation with daily number of building fires. The frequency of urban building fires is found to be significantly correlated with...

Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

NASA Astrophysics Data System (ADS)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.

Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia

NASA Astrophysics Data System (ADS)

Alahmadi, F.; Rahman, N. A.; Abdulrazzak, M.

2014-09-01

Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events. This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city located in the western part of Saudi Arabia. Different statistical distributions were applied (i.e. Normal, Log Normal, Extreme Value type I, Generalized Extreme Value, Pearson Type III, Log Pearson Type III) and their distribution parameters were estimated using L-moments methods. Also, different selection criteria models are applied, e.g. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and Anderson-Darling Criterion (ADC). The analysis indicated the advantage of Generalized Extreme Value as the best fit statistical distribution for Madinah partial duration daily rainfall series. The outcome of such an evaluation can contribute toward better design criteria for flood management, especially flood protection measures.

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

USGS Publications Warehouse

Pervez, Md Shahriar; Henebry, Geoffrey M.

2014-01-01

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change

Instability and its relation to precipitation over the Eastern Iberian Peninsula

NASA Astrophysics Data System (ADS)

Iturrioz, I.; Hernández, E.; Ribera, P.; Queralt, S.

2007-04-01

Synoptic situations producing rainfall at four rawinsonde observatories at eastern Spain are classified as stratiform or convective depending on dynamic and thermodynamic instability indices. Two daily radiosonde and daily-accumulated precipitation data from four observatories in Eastern Spain are used: Madrid-Barajas (MB), Murcia (MU), Palma de Mallorca (PA) and Zaragoza (ZA). We calculated two thermodynamic instability indices from radiosonde data: CAPE and LI. Likewise, from ERA40 reanalysis data we have calculated the Q vector divergence over the Iberian Peninsula and Balearic Islands, as a parameter describing dynamical instability. Synoptic situations producing rainfall were classified as convective or stratiform, satisfying a criterion based on the values of dynamic and thermodynamic indices at each observatory. It is observed that the number of days with stratiform precipitation related to the total number of precipitation days follows a consistent annual pattern.

Observed changes in extreme precipitation in Poland: 1991-2015 versus 1961-1990

NASA Astrophysics Data System (ADS)

Pińskwar, Iwona; Choryński, Adam; Graczyk, Dariusz; Kundzewicz, Zbigniew W.

2018-01-01

Several episodes of extreme precipitation excess and extreme precipitation deficit, with considerable economic and social impacts, have occurred in Europe and in Poland in the last decades. However, the changes of related indices exhibit complex variability. This paper analyses changes in indices related to observed abundance and deficit of precipitated water in Poland. Among studied indices are maximum seasonal 24-h precipitation for the winter half-year (Oct.-March) and the summer half-year (Apr.-Sept.), maximum 5-day precipitation, maximum monthly precipitation and number of days with intense or very intense precipitation (respectively, in excess of 10 mm or 20 mm per day). Also, the warm-seasonal maximum number of consecutive dry days (longest period with daily precipitation below 1 mm) was examined. Analysis of precipitation extremes showed that daily maximum precipitation for the summer half-year increased for many stations, and increases during the summer half-year are more numerous than those in the winter half-year. Also, analysis of 5-day and monthly precipitation sums show increases for many stations. Number of days with intense precipitation increases especially in the north-western part of Poland. The number of consecutive dry days is getting higher for many stations in the summer half-year. Comparison of these two periods: colder 1961-1990 and warmer 1991-2015, revealed that during last 25 years most of statistical indices, such as 25th and 75th percentiles, median, mean and maximum are higher. However, many changes discussed in this paper are weak and statistically insignificant. The findings reported in this paper challenge results based on earlier data that do not include 2007-2015.

Improving Frozen Precipitation Density Estimation in Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sparrow, K.; Fall, G. M.

2017-12-01

The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

Validation and uncertainty analysis for monthly and extreme precipitation in the ERA-20C reanalysis based on the WZN in-situ measurements

NASA Astrophysics Data System (ADS)

Rustemeier, Elke; Ziese, Markus; Raykova, Kristin; Meyer-Christoffer, Anja; Schneider, Udo; Finger, Peter; Becker, Andreas

2017-04-01

The proper representation of precipitation, in particular extreme precipitation, in global reanalyses is still challenging. This paper focuses on the potential of the ERA-20C centennial reanalysis to reproduce precipitation events. The global ERA-20C Reanalysis has been developed within the projects ERA-CLIM and its successor ERA-CLIM2 with the aim of a multi-decadal reanalysis of the global climate system. One of the objectives of ERA-CLIM2 is to provide useful information about the uncertainty of the various parameters. Since precipitation is a prognostic variable, it allows for independent validation by in-situ measurements. For this purpose, the Global Precipitation Climatology Centre (GPCC) operated by the DWD has compared the ERA-20C Reanalysis with the GPCC observational products "Full Data Monthly Version 7" (FDM-V7) and "Full Data Daily Version 1" (FDD-V1). ERA-20C is based on the ECMWF prediction model IFS version Cy38r1 with a spatial resolution of approximately 125 km and covers the 111 years from 1900 to 2010. The GPCC FDM-V7 raster data product, on the other hand, includes the global land surface in-situ measurements between 1901 and 2013 (Schneider et al., 2014) and the FDD-V1 raster data product covers daily precipitation from 1988 to 2013 with daily resolution. The most suitable resolution of 1° was used to validate ERA-20C. For the spatial and temporal validation of the ERA-20C Reanalysis, global temporal scores were calculated on monthly, seasonal and annual time scales. These include e.g. monthly contingency table scores, correlation or climate change indices (ETCCDI) for precipitation to determine extreme values and their temporal change (Peterson et al., 2001, Appendix A). Not surprisingly, the regions with the strongest differences are also those with data scarcity, mountain regions with their luv and lee effects or monsoon areas. They all show a strong systematic difference and breaks within the time series. Differences between ERA-20C and

The Impact of Parameterized Convection on Climatological Precipitation in Atmospheric Global Climate Models

NASA Astrophysics Data System (ADS)

Maher, Penelope; Vallis, Geoffrey K.; Sherwood, Steven C.; Webb, Mark J.; Sansom, Philip G.

2018-04-01

Convective parameterizations are widely believed to be essential for realistic simulations of the atmosphere. However, their deficiencies also result in model biases. The role of convection schemes in modern atmospheric models is examined using Selected Process On/Off Klima Intercomparison Experiment simulations without parameterized convection and forced with observed sea surface temperatures. Convection schemes are not required for reasonable climatological precipitation. However, they are essential for reasonable daily precipitation and constraining extreme daily precipitation that otherwise develops. Systematic effects on lapse rate and humidity are likewise modest compared with the intermodel spread. Without parameterized convection Kelvin waves are more realistic. An unexpectedly large moist Southern Hemisphere storm track bias is identified. This storm track bias persists without convection schemes, as does the double Intertropical Convergence Zone and excessive ocean precipitation biases. This suggests that model biases originate from processes other than convection or that convection schemes are missing key processes.

Precipitation recycling as a mechanism for ecoclimatological stability through local and non-local interactions

NASA Astrophysics Data System (ADS)

Dominguez, Francina

This study is the first to analyze the mechanisms that drive precipitation recycling variability at the daily to intraseasonal timescale. A new Dynamic Precipitation Recycling model is developed which, unlike previous models, includes the moisture storage term in the equation of conservation of atmospheric moisture. As shown using scaling analysis, the moisture storage term is non-negligible at small time scales, so the new model enables us to analyze precipitation recycling variability at shorter timescales than traditional models. The daily to intraseasonal analysis enables us to uncover key relationships between recycling and the moisture and energy fluxes. In the second phase of this work, a spatiotemporal analysis of daily precipitation recycling is performed over two regions of North America: the Midwestern United States, and the North American Monsoon System (NAMS) region. These regions were chosen because they present contrasting land-atmosphere interactions. Different physical mechanisms drive precipitation recycling in each region. In the Midwestern United States, evapotranspiration is not significantly affected by soil moisture anomalies, and there is a high recycling ratio during periods of reduced total precipitation. The reason is that, during periods of drier atmospheric conditions, transpiration will continue to provide moisture to the overlying atmosphere and contribute to total rainfall. Consequently, precipitation recycling variability in not driven by changes in evapotranspiration. Precipitable water, sensible heat and moisture fluxes are the main drivers of recycling variability in the Midwest. However, the drier soil moisture conditions over the NAMS region limit evapotranspiration, which will drive recycling variability. In this region, evapotranspiration becomes an important contribution to precipitation after Monsoon onset when total precipitation and evapotranspiration are highest. The precipitation recycling process in the NAMS region

Systematic recover of long high-resolution rainfall time series recorded by pluviographs during the 20th century.

NASA Astrophysics Data System (ADS)

Delitala, Alessandro M. S.; Deidda, Roberto; Mascaro, Giuseppe; Piga, Enrico; Querzoli, Giorgio

2010-05-01

During most of the 20th century, precipitation has been continuously measured by means of the so-called "pluviographs", i.e. rain gauges including a mechanical apparatus for continuously recording the depth of water from precipitation on specific strip charts, usually on a weekly basis. The signal recorded on such strips was visually examined by trained personnel on a regular basis, in order to extract the daily precipitation totals and the maximum precipitation intensities over short periods (from a few minutes to hours). The rest of the high-resolution information contained in the signal was usually not extracted, except for specific cases. A systematic recovering of the entire information at high temporal resolution contained in these precipitation signals would provide a fundamental database to improve the characterization of historical rainfall climatology during the previous century. The Department of Land Engineering of the University of Cagliari has recently developed and tested an automatic software, based on image analysis techniques, which is able to acquire the scanned images of the pluviograph strip charts, to automatically digitise the signal and to produce a digital database of continuous precipitation records at the highest possible temporal resolution, i.e. 5 to 10 minutes. Along with that, a significant amount of daily precipitation totals from the late 19th and the 20th century, either elaborated from pluviograph strip charts or simply derived from bucket rain gauges, still exists in paper form, but it has never been digitalized. Within a project partly-funded by the Operational Programme of the European Union "Italia-Francia Marittimo", the Regional Environmental Protection Agency of Sardinia and the University of Cagliari will recover both the high-resolution rainfall signals and the older time series of daily totals recorded by a large number of pluviographs belonging to the historical monitoring networks of the island of Sardinia. Such data

Validation of High Resolution Orbital Precipitation Over Upper Mahanadi River Basin, India

NASA Astrophysics Data System (ADS)

Gautam, A. K.; Pandey, A.

2016-12-01

Precipitation is one of the most important component of hydrologic cycle and used for various applications i.e. hydrological modeling, structure design to water management policy. Satellite based precipitation, radar rainfall and rain-gauge networks are supporting to each other, in relation to their spatial coverage and ability of observing precipitation. In the absence of rainfall data, satellite precipitation products can be used in the developing countries and over complex terrain where precipitation observations are either sparse or not available. However, satellite precipitation estimates are affected by different errors (AghaKouchak, et al., 2012.). Therefore, ground validation of satellite precipitation estimates is essential. In this study, the upper Mahanadi River Basin (A Part of Central India), has been selected for evaluation of the TRMM multi-satellite precipitation analysis (TMPA) and IMERG (Integrated Multi-satellite Retrievals for GPM) satellite Based Precipitation Products for the period of April 2014 - December 2015. The TMPA (3B42V7) and IMERG (late run) precipitation estimates were evaluated using statistical, contingency table and volumetric method for available 112 rain gauge stations in the study area. Results indicated that, both IMERG and TMPA precipitation overestimated the daily precipitation. The results also revealed that IMERG precipitation estimates provide better accuracy than TMPA precipitation estimates for very light rain (0.1-2.5 mm day-1), light rain (2.5-7.5 mm day-1), moderate rain (7.5-35.5 mm day-1), heavy rain (35.5-64.5 mm day-1) and very heavy rain (>64.5 mm day-1). Although, the detection capability of daily TMPA precipitation performed better in heavy rain. The results showed a good correlation (as high as 0.84) and poor correlation (as low as 0.012) with GPM satellite data over the most parts of the study area. The analyses suggest that, there is a need for improvement in precipitation estimation algorithm and accuracy

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred A.

2014-01-01

High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.

Large-Scale Meteorological Patterns Associated with Extreme Precipitation in the US Northeast

NASA Astrophysics Data System (ADS)

Agel, L. A.; Barlow, M. A.

2016-12-01

Patterns of daily large-scale circulation associated with Northeast US extreme precipitation are identified using both k-means clustering (KMC) and Self-Organizing Maps (SOM) applied to tropopause height. Tropopause height provides a compact representation of large-scale circulation patterns, as it is linked to mid-level circulation, low-level thermal contrasts and low-level diabatic heating. Extreme precipitation is defined as the top 1% of daily wet-day observations at 35 Northeast stations, 1979-2008. KMC is applied on extreme precipitation days only, while the SOM algorithm is applied to all days in order to place the extreme results into a larger context. Six tropopause patterns are identified on extreme days: a summertime tropopause ridge, a summertime shallow trough/ridge, a summertime shallow eastern US trough, a deeper wintertime eastern US trough, and two versions of a deep cold-weather trough located across the east-central US. Thirty SOM patterns for all days are identified. Results for all days show that 6 SOM patterns account for almost half of the extreme days, although extreme precipitation occurs in all SOM patterns. The same SOM patterns associated with extreme precipitation also routinely produce non-extreme precipitation; however, on extreme precipitation days the troughs, on average, are deeper and the downstream ridges more pronounced. Analysis of other fields associated with the large-scale patterns show various degrees of anomalously strong upward motion during, and moisture transport preceding, extreme precipitation events.

The Precipitation Characteristics of ISCCP Tropical Weather States

NASA Technical Reports Server (NTRS)

Lee, Dongmin; Oreopoulos, Lazaros; Huffman, George J.; Rossow, William B.; Kang, In-Sik

2011-01-01

We examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35 deg S to 35 deg N) for a 10-year period. Our main precipitation data set is the TRMM Multisatellite Precipitation Analysis 3B42 data set, but Global Precipitation Climatology Project daily data are also used for comparison. We find that the most convective weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropical zone of our study; yet, even this weather state appears to not precipitate about half the time. WS1 exhibits a modest annual cycle of domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states tend to be stronger when occurring before or after WS1. The relative contribution of the various weather states to total precipitation is different between ocean and land, with WS1 producing more intense precipitation on average over ocean than land. The results of this study, in addition to advancing our understanding of the current state of tropical precipitation, can serve as a higher order diagnostic test on whether it is distributed realistically among different weather states in atmospheric models.

Development of Sub-Daily Intensity Duration Frequency (IDF) Curves for Major Urban Areas in India

NASA Astrophysics Data System (ADS)

Ali, H.; Mishra, V.

2014-12-01

Extreme precipitation events disrupt urban transportation and cause enormous damage to infrastructure. Urban areas are fast responding catchments due to significant impervious surface. Stormwater designs based on daily rainfall data provide inadequate information. We, therefore, develop intensity-duration-frequency curves using sub-daily (1 hour to 12 hour) rainfall data for 57 major urban areas in India. While rain gage stations data from urban areas are most suitable, but stations are unevenly distributed and their data have gaps and inconsistencies. Therefore, we used hourly rainfall data from the Modern Era Retrospective-analysis for Research and Applications (MERRA), which provides a long term data (1979 onwards). Since reanalysis products have uncertainty associated with them we need to enhance their accuracy before their application. We compared daily rain gage station data obtained from Global Surface Summary of Day Data (GSOD) available for 65 stations for the period of 2000-2010 with gridded daily rainfall data provided by Indian Meteorological Department (IMD). 3-hourly data from NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were aggregated to daily for comparison with GSOD station data . TMPA is found to be best correlated with GSOD data. We used TMPA data to correct MERRA's hourly precipitation, which were applied to develop IDF curves. We compared results with IDF curves from empirical methods and found substantial disparities in the existing stormwater designs in India.

Does extreme precipitation intensity depend on the emissions scenario?

NASA Astrophysics Data System (ADS)

Pendergrass, Angeline; Lehner, Flavio; Sanderson, Benjamin; Xu, Yangyang

2016-04-01

The rate of increase of global-mean precipitation per degree surface temperature increase differs for greenhouse gas and aerosol forcings, and therefore depends on the change in composition of the emissions scenario used to drive climate model simulations for the remainder of the century. We investigate whether or not this is also the case for extreme precipitation simulated by a multi-model ensemble driven by four realistic emissions scenarios. In most models, the rate of increase of maximum annual daily rainfall per degree global warming in the multi-model ensemble is statistically indistinguishable across the four scenarios, whether this extreme precipitation is calculated globally, over all land, or over extra-tropical land. These results indicate that, in most models, extreme precipitation depends on the total amount of warming and does not depend on emissions scenario, in contrast to mean precipitation.

Acid Precipitation: Causes and Consequences.

ERIC Educational Resources Information Center

Babich, Harvey; And Others

1980-01-01

This article is the first of three articles in a series on the acid rain problem in recent years. Discussed are the causes of acid precipitation and its consequences for the abiotic and biotic components of the terrestrial and aquatic ecosystems, and for man-made materials. (Author/SA)

North-South precipitation patterns in western North America on interannual-to-decadal timescales

USGS Publications Warehouse

Dettinger, M.D.; Cayan, D.R.; Diaz, Henry F.; Meko, D.M.

1998-01-01

The overall amount of precipitation deposited along the West Coast and western cordillera of North America from 25??to 55??N varies from year to year, and superimposed on this domain-average variability are varying north-south contrasts on timescales from at least interannual to interdecadal. In order to better understand the north-south precipitation contrasts, their interannual and decadal variations are studied in terms of how much they affect overall precipitation amounts and how they are related to large-scale climatic patterns. Spatial empirical orthogonal functions (EOFs) and spatial moments (domain average, central latitude, and latitudinal spread) of zonally averaged precipitation anomalies along the westernmost parts of North America are analyzed, and each is correlated with global sea level pressure (SLP) and sea surface temperature series, on interannual (defined here as 3-7 yr) and decadal (>7 yr) timescales. The interannual band considered here corresponds to timescales that are particularly strong in tropical climate variations and thus is expected to contain much precipitation variability that is related to El Nino-Southern Oscillation; the decadal scale is defined so as to capture the whole range of long-term climatic variations affecting western North America. Zonal EOFs of the interannual and decadal filtered versions of the zonal-precipitation series are remarkably similar. At both timescales, two leading EOFs describe 1) a north-south seesaw of precipitation pivoting near 40??N and 2) variations in precipitation near 40??N, respectively. The amount of overall precipitation variability is only about 10% of the mean and is largely determined by precipitation variations around 40??-45??N and most consistently influenced by nearby circulation patterns; in this sense, domain-average precipitation is closely related to the second EOF. The central latitude and latitudinal spread of precipitation distributions are strongly influenced by precipitation

Global Precipitation Measurement. Report 7; Bridging from TRMM to GPM to 3-Hourly Precipitation Estimates

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Smith, Eric A.; Adams, W. James (Editor)

2002-01-01

Historically, multi-decadal measurements of precipitation from surface-based rain gauges have been available over continents. However oceans remained largely unobserved prior to the beginning of the satellite era. Only after the launch of the first Defense Meteorological Satellite Program (DMSP) satellite in 1987 carrying a well-calibrated and multi-frequency passive microwave radiometer called Special Sensor Microwave/Imager (SSM/I) have systematic and accurate precipitation measurements over oceans become available on a regular basis; see Smith et al. (1994, 1998). Recognizing that satellite-based data are a foremost tool for measuring precipitation, NASA initiated a new research program to measure precipitation from space under its Mission to Planet Earth program in the 1990s. As a result, the Tropical Rainfall Measuring Mission (TRMM), a collaborative mission between NASA and NASDA, was launched in 1997 to measure tropical and subtropical rain. See Simpson et al. (1996) and Kummerow et al. (2000). Motivated by the success of TRMM, and recognizing the need for more comprehensive global precipitation measurements, NASA and NASDA have now planned a new mission, i.e., the Global Precipitation Measurement (GPM) mission. The primary goal of GPM is to extend TRMM's rainfall time series while making substantial improvements in precipitation observations, specifically in terms of measurement accuracy, sampling frequency, Earth coverage, and spatial resolution. This report addresses four fundamental questions related to the transition from current to future global precipitation observations as denoted by the TRMM and GPM eras, respectively.

Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901-2002), West Bengal, India

NASA Astrophysics Data System (ADS)

Chatterjee, Soumendu; Khan, Ansar; Akbari, Hashem; Wang, Yupeng

2016-12-01

This paper intended to investigate spatio-temporal monotonic trend and shift in concentration of monsoon precipitation across West Bengal, India, by analysing the time series of monthly precipitation from 18 weather stations during the period from 1901 to 2002. In dealing with, the inhomogeneity in the precipitation series, RHtestsV4 software package is used to detect, and adjust for, multiple change points (shifts) that could exist in data series. Finally, the cumulative deviation test was applied at 5% significant level to check the homogeneity (presence of historic changes by cumulative deviations test). Afterward, non-parametric Mann-Kendall (MK) test and Theil-Sen estimator (TSE) was applied to detect of nature and slope of trends; and, Sequential Mann Kendall (SQMK) test was applied for detection of turning point and magnitude of change in trends. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation in precipitation data series. Four indices- precipitation concentration index (PCI), precipitation concentration degree (PCD), precipitation concentration period (PCP) and fulcrum (centre of gravity) were used to detect precipitation concentration and the spatial pattern in it. The application of the above-mentioned procedures has shown very notable statewide monotonic trend for monsoon precipitation time series. Regional cluster analysis by SQMK found increasing precipitation in mountain and coastal regions in general, except during the non- monsoon seasons. The results show that higher PCI values were mainly observed in South Bengal, whereas lower PCI values were mostly detected in North Bengal. The PCI values are noticeably larger in places where both monsoon total precipitation and span of rainy season are lower. The results of PCP reveal that precipitation in Gangetic Bengal mostly occurs in summer (monsoon season), and the rainy season arrives earlier in North Bengal than South Bengal

Precipitation interpolation in mountainous areas

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2015-04-01

Different precipitation interpolation techniques as well as external drift covariates are tested and compared in a 26000 km2 mountainous area in Norway, using daily data from 60 stations. The main method of assessment is cross-validation. Annual precipitation in the area varies from below 500 mm to more than 2000 mm. The data were corrected for wind-driven undercatch according to operational standards. While temporal evaluation produce seemingly acceptable at-station correlation values (on average around 0.6), the average daily spatial correlation is less than 0.1. Penalising also bias, Nash-Sutcliffe R2 values are negative for spatial correspondence, and around 0.15 for temporal. Despite largely violated assumptions, plain Kriging produces better results than simple inverse distance weighting. More surprisingly, the presumably 'worst-case' benchmark of no interpolation at all, simply averaging all 60 stations for each day, actually outperformed the standard interpolation techniques. For logistic reasons, high altitudes are under-represented in the gauge network. The possible effect of this was investigated by a) fitting a precipitation lapse rate as an external drift, and b) applying a linear model of orographic enhancement (Smith and Barstad, 2004). These techniques improved the results only marginally. The gauge density in the region is one for each 433 km2; higher than the overall density of the Norwegian national network. Admittedly the cross-validation technique reduces the gauge density, still the results suggest that we are far from able to provide hydrological models with adequate data for the main driving force.

Modeling winter precipitation over the Juneau Icefield, Alaska, using a linear model of orographic precipitation

NASA Astrophysics Data System (ADS)

Roth, Aurora; Hock, Regine; Schuler, Thomas V.; Bieniek, Peter A.; Pelto, Mauri; Aschwanden, Andy

2018-03-01

Assessing and modeling precipitation in mountainous areas remains a major challenge in glacier mass balance modeling. Observations are typically scarce and reanalysis data and similar climate products are too coarse to accurately capture orographic effects. Here we use the linear theory of orographic precipitation model (LT model) to downscale winter precipitation from a regional climate model over the Juneau Icefield, one of the largest ice masses in North America (>4000 km2), for the period 1979-2013. The LT model is physically-based yet computationally efficient, combining airflow dynamics and simple cloud microphysics. The resulting 1 km resolution precipitation fields show substantially reduced precipitation on the northeastern portion of the icefield compared to the southwestern side, a pattern that is not well captured in the coarse resolution (20 km) WRF data. Net snow accumulation derived from the LT model precipitation agrees well with point observations across the icefield. To investigate the robustness of the LT model results, we perform a series of sensitivity experiments varying hydrometeor fall speeds, the horizontal resolution of the underlying grid, and the source of the meteorological forcing data. The resulting normalized spatial precipitation pattern is similar for all sensitivity experiments, but local precipitation amounts vary strongly, with greatest sensitivity to variations in snow fall speed. Results indicate that the LT model has great potential to provide improved spatial patterns of winter precipitation for glacier mass balance modeling purposes in complex terrain, but ground observations are necessary to constrain model parameters to match total amounts.

Precipitation estimates and comparison of satellite rainfall data to in situ rain gauge observations to further develop the watershed-modeling capabilities for the Lower Mekong River Basin

NASA Astrophysics Data System (ADS)

Dandridge, C.; Lakshmi, V.; Sutton, J. R. P.; Bolten, J. D.

2017-12-01

This study focuses on the lower region of the Mekong River Basin (MRB), an area including Burma, Cambodia, Vietnam, Laos, and Thailand. This region is home to expansive agriculture that relies heavily on annual precipitation over the basin for its prosperity. Annual precipitation amounts are regulated by the global monsoon system and therefore vary throughout the year. This research will lead to improved prediction of floods and management of floodwaters for the MRB. We compare different satellite estimates of precipitation to each other and to in-situ precipitation estimates for the Mekong River Basin. These comparisons will help us determine which satellite precipitation estimates are better at predicting precipitation in the MRB and will help further our understanding of watershed-modeling capabilities for the basin. In this study we use: 1) NOAA's PERSIANN daily 0.25° precipitation estimate Climate Data Record (CDR), 2) NASA's Tropical Rainfall Measuring Mission (TRMM) daily 0.25° estimate, and 3) NASA's Global Precipitation Measurement (GPM) daily 0.1 estimate and 4) 488 in-situ stations located in the lower MRB provide daily precipitation estimates. The PERSIANN CDR precipitation estimate was able to provide the longest data record because it is available from 1983 to present. The TRMM precipitation estimate is available from 2000 to present and the GPM precipitation estimates are available from 2015 to present. It is for this reason that we provide several comparisons between our precipitation estimates. Comparisons were done between each satellite product and the in-situ precipitation estimates based on geographical location and date using the entire available data record for each satellite product for daily, monthly, and yearly precipitation estimates. We found that monthly PERSIANN precipitation estimates were able to explain up to 90% of the variability in station precipitation depending on station location.

A daily Azores-Iceland North Atlantic Oscillation index back to 1850.

PubMed

Cropper, Thomas; Hanna, Edward; Valente, Maria Antónia; Jónsson, Trausti

2015-07-01

We present the construction of a continuous, daily (09:00 UTC), station-based (Azores-Iceland) North Atlantic Oscillation (NAO) Index back to 1871 which is extended back to 1850 with additional daily mean data. The constructed index more than doubles the length of previously existing, widely available, daily NAO time series. The index is created using entirely observational sea-level pressure (SLP) data from Iceland and 73.5% of observational SLP data from the Azores - the remainder being filled in via reanalysis (Twentieth Century Reanalysis Project and European Mean Sea Level Pressure) SLP data. Icelandic data are taken from the Southwest Iceland pressure series. We construct and document a new Ponta Delgada SLP time series based on recently digitized and newly available data that extend back to 1872. The Ponta Delgada time series is created by splicing together several fractured records (from Ponta Delgada, Lajes, and Santa Maria) and filling in the major gaps (pre-1872, 1888-1905, and 1940-1941) and occasional days (145) with reanalysis data. Further homogeneity corrections are applied to the Azores record, and the daily (09:00 UTC) NAO index is then calculated. The resulting index, with its extended temporal length and daily resolution, is the first reconstruction of daily NAO back into the 19th Century and therefore is useful for researchers across multiple disciplines.

Variability of thermal and precipitation conditions in the growing season in Poland in the years 1966-2015

NASA Astrophysics Data System (ADS)

Tomczyk, Arkadiusz M.; Szyga-Pluta, Katarzyna

2018-03-01

The aim of the study was to identify the thermal and precipitation conditions and their changes in the growing season in Poland in the years 1966-2015. Data on average daily air temperature and daily precipitation totals for 30 stations from the period of 1966-2015 were used. The data were obtained from the collections of the Institute of Meteorology and Water Management—National Research Institute. The growing season was defined as the period of average daily air temperature ≥ 5 °C. The mathematical formulas proposed by Gumiński (1948) were used to determine its start and end dates. In the growing season in Poland in the years 1966-2015, there were more significant changes in the thermal conditions than there were in the precipitation conditions. In terms of long-term trends over the study period, thermal conditions during the growing season are characterised by an increase in mean air temperature, an increase in the sum of air temperatures and an increasing occurrence of seasons classified as above-normal seasons. Precipitation conditions of the growing season show large temporal and spatial variations in precipitation and a predominance of normal conditions. The changes in precipitation were not statistically significant, except for Świnoujście.

Evaluation of high resolution global satellite precipitation products using daily raingauge data over the Upper Blue Nile Basin

NASA Astrophysics Data System (ADS)

Sahlu, Dejene; Moges, Semu; Anagnostou, Emmanouil; Nikolopoulos, Efthymios; Hailu, Dereje; Mei, Yiwen

2017-04-01

Water resources assessment, planning and management in Africa is often constrained by the lack of reliable spatio-temporal rainfall data. Satellite products are steadily growing and offering useful alternative datasets of rainfall globally. The aim of this paper is to examine the error characteristics of the main available global satellite precipitation products with the view of improving the reliability of wet season (June to September) and small rainy season rainfall datasets over the Upper Blue Nile Basin. The study utilized six satellite derived precipitation datasets at 0.25-deg spatial grid size and daily temporal resolution:1) the near real-time (3B42_RT) and gauge adjusted (3B42_V7) products of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), 2) gauge adjusted and unadjusted Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products and 3) the gauge adjusted and un-adjusted product of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Morphing technique (CMORPH) over the period of 2000 to 2013.The error analysis utilized statistical techniques using bias ratio (Bias), correlation coefficient (CC) and root-mean-square-error (RMSE). Mean relative error (MRE), CC and RMSE metrics are further examined for six categories of 10th, 25th, 50th, 75th, 90thand 95th percentile rainfall thresholds. The skill of the satellite estimates is evaluated using categorical error metrics of missed rainfall volume fraction (MRV), falsely detected rainfall volume fraction (FRV), probability of detection (POD) and False Alarm Ratio (FAR). Results showed that six satellite based rainfall products underestimated wet season (June to September) gauge precipitation, with the exception of non-adjusted PERSIANN that overestimated the initial part of the rainy season (March to May). During the wet season, adjusted CMORPH has relatively better bias ratio (89

Regional scenarios of mean and extreme precipitation regimes in the Basque Country

NASA Astrophysics Data System (ADS)

Moncho, Roberto; Chust, Guillem; Caselles, Vicente

2010-05-01

According to different regional projections of climate change for the 21st century, changes in the mean and extreme precipitation regimes are expected in most of Europe (Christensen et al., 2007). Precipitation extreme events, in particular, can generate important natural hazards and associated social impacts. such as increasing the probability of flooding events. The objective of this paper is to calibrate the regional models for mean and extreme precipitation regimes through a reference time series (1961-2000) in the Basque Country. The reference time series have been obtained previously from a spatially reconstruction with a Digital Terrain Model and a multiple regression model. In this study, we have used four regional climate models of ENSEMBLE project: METNO-HIRHAM, UCLM-PROMES, KNMI-RAKMO2 and CNRM-RM4.5, under A1B scenario and the ERA40 climate reanalysis. The analysis of extreme precipitation has been based on a relationship between the intensity-duration-frequency (IDF) curves and the Main-Average-Intensity (MAI) curves (Moncho et al., 2009). The regional climate models showed no significant change in mean annual precipitation in the Basque Country for the period 1961-2000 (0 ± 3% decade-1). This result is consistent with the trend of the reference series, which was not significant (-1 ± 3% decade-1, p-value = 0.51). For the period of 2001 to 2050, the calibration of the model ensemble showed no significant change in trend (-1 ± 3% decade-1, p-value = 0.35). However, some models showed a significant change in mean precipitation from 1961-2000 to 2001-2050 (METNO-HIRHAM, -10 ± 5%, p-value = 0.019) and from 2051-2100 (KNMI-RAKMO2, -8 ± 3%, p-value = 0.007). The model that best fits the reference period 1961-2000 for extreme precipitation was the METNO-HIRHAM model, followed by the UCLM-PROMES and KNMI-RAKMO2 models, therefore, these models would best describe the possible changes in future regimes. After calibrating the projections of the heavy

Observed variability of summer precipitation pattern and extreme events in East China associated with variations of the East Asian summer monsoon: VARIABILITY OF SUMMER PRECIPITATION AND EXTREME EVENT IN EAST CHINA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Lei; Qian, Yun; Zhang, Yaocun

This paper presents a comprehensive analysis of interannual and interdecadal variations of summer precipitation and precipitation-related extreme events in China associated with variations of the East Asian summer monsoon (EASM) from 1979-2012. A high-quality daily precipitation dataset covering 2287 weather stations in China is analyzed. Based on the precipitation pattern analysis using empirical orthogonal functions, three sub-periods of 1979-1992 (period I), 1993-1999 (period II) and 2000-2012 (period III) are identified to be representative of the precipitation variability. Similar significant variability of the extreme precipitation indices is found across four sub-regions in eastern China. The spatial patterns of summer mean precipitation,more » the number of days with daily rainfall exceeding 95th percentile precipitation (R95p) and the maximum number of consecutive wet days (CWD) anomalies are consistent, but opposite to that of maximum consecutive dry days (CDD) anomalies during the three sub-periods. However, the spatial patterns of hydroclimatic intensity (HY-INT) are notably different from that of the other three extreme indices, but highly correlated to the dry events. The changes of precipitation anomaly patterns are accompanied by the change of the EASM regime and the abrupt shift of the position of the west Pacific subtropical high around 1992/1993 and 1999/2000, respectively, which influence the moisture transport that contributes most to the precipitation anomalies. Lastly, the EASM intensity is linked to sea surface temperature anomaly over the tropical Indian and Pacific Ocean that influences deep convection over the oceans.« less

On the relationship between atmospheric rivers (ARs) and heavy precipitation over Japan

NASA Astrophysics Data System (ADS)

Yatagai, A. I.; Takayabu, Y. N.

2016-12-01

Atmospheric Rivers (ARs) are known as the water-vapor rich part of the broader warm conveyor belt. Recently, several AR detection algorithms are proposed, and structures and that of statistical features are studied globally. Since Japan is a humid country located in the north of the warm pool, ARs, middle tropospheric fast moisture transport, might be an important moisture source for heavy precipitation events in Japan. The purpose of this study is to develop an algorithm of detection of ARs over Japan, and to investigate the possible relationship between them and Japanese heavy precipitation events. Since high spatial correlations were obtained between ERA-Interim reanalysis PW and that of SSM/I (microwave images), we used daily PW (0.75 degree grid) for detection of the ARs. Using 36 years (1979-2014) ERA-Interim, we defined daily smoothed PW climatology. Then, we detected AR area with daily anomaly of PW exceeding 10 mm. However, we exclude round-shaped (caused by Typhoon etc) area and the case of moisture transport not exceeding 30N/30S. The daily AR events over Japan (123-146E, 24-46N) are; 1013 cases for winter (DJF), 1722 for spring (MAM), 2229 for summer (JJA) and 1870 for autumn (SON) during the 36 years. They successfully include Hiroshima disaster event (19 August 2014, Hirota et al., 2015) and Amami heavy precipitation event (20 October 2010). The summer with large AR appearance (1998 and 2010) had negative SOI (La Nina), and lowest appearance year (1992) was the year of El Nino (positively significant SOI). Totally, more ARs come over Japan area in La Nina years, however, the seasonal statistics between SOI and the number of AR is not straightforward, indicating that it is difficult to explain ARs over Japan with only tropical inter-annual variability. We use APHRO-JP (Kamiguchi et al., 2010) daily gridded (0.05 degree) precipitation (1979-2011) over Japanese land areas for comparison. Among the 32 years (1979-2011), we had 82 cases of heavy

Gridded precipitation fields at high temporal and spatial resolution for operational flood forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

van Osnabrugge, Bart; Weerts, Albrecht; Uijlenhoet, Remko

2017-04-01

Gridded areal precipitation, as one of the most important hydrometeorological input variables for initial state estimation in operational hydrological forecasting, is available in the form of raster data sets (e.g. HYRAS and EOBS) for the River Rhine basin. These datasets are compiled off-line on a daily time step using station data with the highest possible spatial density. However, such a product is not available operationally and at an hourly discretisation. Therefore, we constructed an hourly gridded precipitation dataset at 1.44 km2 resolution for the Rhine basin for the period from 1998 to present using a REGNIE-like interpolation procedure (Weerts et al., 2008) using a low and a high density rain gauge network. The datasets were validated against daily HYRAS (Rauthe, 2013) and EOBS (Haylock, 2008) data. The main goal of the operational procedure is to emulate the HYRAS dataset as good as possible, as the daily HYRAS dataset is used in the off-line calibration of the hydrological model. Our main findings are that even with low station density, the spatial patterns found in the HYRAS data set are well reproduced. With low station density (years 1999-2006) our dataset underestimates precipitation compared to HYRAS and EOBS, notably during the winter. However, interpolation based on the same set of stations overestimates precipitation compared to EOBS for the years 2006-2014. This discrepancy disappears when switching to the high station density. We also analyze the robustness of the hourly precipitation fields by comparing with stations not used during interpolation. Specific issues regarding the data when creating the gridded precipitation fields will be highlighted. Finally, the datasets are used to drive an hourly and daily gridded WFLOW_HBV model of the Rhine at the same spatial resolution. Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and

Precipitation in Madeira island and atmospheric rivers in the winter seasons

NASA Astrophysics Data System (ADS)

Couto, Flavio T.; Salgado, Rui; João Costa, Maria; Prior, Victor

2016-04-01

This study aims to analyse the distribution of the daily accumulated precipitation in the Madeira's highlands over a 10-year period, as well as the main characteristics associated with atmospheric rivers (ARs) affecting the island during 10 winter seasons, and their impact in the rainfall amounts recorded near the mountain crest in the south-eastern part of the island. The period between September 2002 and November 2012 is considered for the analysis. The ARs have been identified from the total precipitable water vapour field extracted from the Atmospheric Infrared Sounder (AIRS). The AIRS observations were downloaded for a domain covering large part of the North Atlantic Ocean. The precipitable water vapour field from the European Centre for Medium-range Weather Forecasts (ECMWF) analysis was also used aiming to support the AIRS data when there was no satellite information over the island. The daily accumulated precipitation at surface showed generally drier summers, while the highest accumulated precipitation are recorded mainly during the winter, although some significant events may occur also in autumn and spring seasons. The patterns of the precipitable water vapour field when ARs reach the island were investigated, and even if great part of the atmospheric rivers reaches the island in a dissipation stage, some rivers are heavy enough to reach the Madeira Island. In this situation, the water vapour transport could be observed in two main configurations and transporting significant water vapour amounts toward the Madeira from the tropical region. This study lead to conclude that the atmospheric rivers, when associated to high values of precipitable water vapour over the island can provide favourable conditions to the development of precipitation, sometimes associated with high amounts. However, it was also found that many cases of high to extreme accumulated precipitation at the surface were not associated to this kind of moisture transport.

Stable Isotopes of Precipitation During Tropical Sumatra Squalls in Singapore

NASA Astrophysics Data System (ADS)

He, Shaoneng; Goodkin, Nathalie F.; Kurita, Naoyuki; Wang, Xianfeng; Rubin, Charles Martin

2018-04-01

Sumatra Squalls, organized bands of thunderstorms, are the dominant mesoscale convective systems during the intermonsoon and southwest monsoon seasons in Singapore. To understand how they affect precipitation isotopes, we monitored the δ value of precipitation daily and continuously (every second and integrated over 30 s) during all squalls in 2015. We found that precipitation δ18O values mainly exhibit a "V"-shape pattern and less commonly a "W"-shape pattern. Variation in δ18O values during a single event is about 1 to 6‰ with the lowest values mostly observed in the stratiform zone, which agrees with previous observations and modeling simulations. Reevaporation can significantly affect δ values, especially in the last stage of the stratiform zone. Daily precipitation is characterized by periodic negative shifts in δ value, largely associated with the squalls rather than moisture source change. The shifts can be more than 10‰, larger than intraevent variation. Initial δ18O values of events are highly variable, and those with the lowest values also have the lowest initial values. Therefore, past convective activities in the upwind area can significantly affect the δ18O, and convection at the sampling site has limited contribution to isotopic variability. A significant correlation between precipitation δ18O value and regional outgoing longwave radiation and rainfall in the Asian monsoon region and western Pacific suggests that regional organized convection probably drives stable isotopic compositions of precipitation. A drop in the frequency of the squalls in 2015 is related to weak organized convection in the region caused by El Niño.

TRMM .25 deg x .25 deg Gridded Precipitation Text Product

NASA Technical Reports Server (NTRS)

Stocker, Erich; Kelley, Owen

2009-01-01

Since the launch of the Tropical Rainfall Measuring Mission (TRMM), the Precipitation Measurement Missions science team has endeavored to provide TRMM precipitation retrievals in a variety of formats that are more easily usable by the broad science community than the standard Hierarchical Data Format (HDF) in which TRMM data is produced and archived. At the request of users, the Precipitation Processing System (PPS) has developed a .25 x .25 gridded product in an easily used ASCII text format. The entire TRMM mission data has been made available in this format. The paper provides the details of this new precipitation product that is designated with the TRMM designator 3G68.25. The format is packaged into daily files. It provides hourly precipitation information from the TRMM microwave imager (TMI), precipitation radar (PR), and TMI/PR combined rain retrievals. A major advantage of this approach is the inclusion only of rain data, compression when a particular grid has no rain from the PR or combined, and its direct ASCII text format. For those interested only in rain retrievals and whether rain is convection or stratiform, these products provide a huge reduction in the data volume inherent in the standard TRMM products. This paper provides examples of the 3G68 data products and their uses. It also provides information about C tools that can be used to aggregate daily files into larger time samples. In addition, it describes the possibilities inherent in the spatial sampling which allows resampling into coarser spatial sampling. The paper concludes with information about downloading the gridded text data products.

Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China

NASA Astrophysics Data System (ADS)

Guo, Hao; Chen, Sheng; Bao, Anming; Behrangi, Ali; Hong, Yang; Ndayisaba, Felix; Hu, Junjun; Stepanian, Phillip M.

2016-07-01

Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015. The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond.

The Relationships between Weather-Related Factors and Daily Outdoor Physical Activity Counts on an Urban Greenway

PubMed Central

Wolff, Dana; Fitzhugh, Eugene C.

2011-01-01

The purpose of this study was to examine relationships between weather and outdoor physical activity (PA). An online weather source was used to obtain daily max temperature [DMT], precipitation, and wind speed. An infra-red trail counter provided data on daily trail use along a greenway, over a 2-year period. Multiple regression analysis was used to examine associations between PA and weather, while controlling for day of the week and month of the year. The overall regression model explained 77.0% of the variance in daily PA (p < 0.001). DMT (b = 10.5), max temp-squared (b = −4.0), precipitation (b = −70.0), and max wind speed (b = 1.9) contributed significantly. Conclusion: Aggregated daily data can detect relationships between weather and outdoor PA. PMID:21556205

Validation of satellite based precipitation over diverse topography of Pakistan

NASA Astrophysics Data System (ADS)

Iqbal, Muhammad Farooq; Athar, H.

2018-03-01

This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product data with 0.25° × 0.25° spatial and post-real-time 3 h temporal resolution using point-based Surface Precipitation Gauge (SPG) data from 40 stations, for the period 1998-2013, and using gridded Asian Precipitation ˗ Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) data abbreviated as APH data with 0.25° × 0.25° spatial and daily temporal resolution for the period 1998-2007, over vulnerable and data sparse regions of Pakistan (24-37° N and 62-75° E). To evaluate the performance of TMPA relative to SPG and APH, four commonly used statistical indicator metrics including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC) are employed on daily, monthly, seasonal as well as on annual timescales. The TMPA slightly overestimated both SPG and APH at daily, monthly, and annual timescales, however close results were obtained between TMPA and SPG as compared to those between TMPA and APH, on the same timescale. The TMPA overestimated both SPG and APH during the Pre-Monsoon and Monsoon seasons, whereas it underestimated during the Post-Monsoon and Winter seasons, with different magnitudes. Agreement between TMPA and SPG was good in plain and medium elevation regions, whereas TMPA overestimated APH in 31 stations. The magnitudes of MAE and RMSE were high at daily timescale as compared to monthly and annual timescales. Relatively large MAE was observed in stations located over high elevation regions, whereas minor MAE was recorded in plain area stations at daily, monthly, and annual timescales. A strong positive linear relationship between TMPA and SPG was established at monthly (0.98), seasonal (0.93 to 0.98) and annual (0.97) timescales. Precipitation increased with the increase of elevation, and not only elevation but latitude also affected the

Precipitation information from GNSS Polarimetric Radio Occultation observations

NASA Astrophysics Data System (ADS)

Padulles, R.; Cardellach, E.; Turk, J.; Tomás, S.; Ao, C. O.; de la Torre-Juárez, M.

2017-12-01

There is currently a gap in satellite observations of the moisture structure during heavy precipitation conditions, since infrared and microwave sounders cannot sense water vapor structure near the surface in the presence of intense precipitation. Conversely, Global Navigation Satellite System (GNSS) Radio Occultations (RO) can profile the moisture structure with high precision and vertical resolution, but cannot directly indicate the presence of precipitation. Polarimetric RO (PRO) measurements have been proposed as a method to characterize heavy rain in GNSS RO, by measuring the polarimetric differential phase delay induced by large size hydrometeors. The PRO concept will be tested from space for the first time on board the Spanish PAZ satellite, planned for launch by the end of 2017. Therefore, for the first time ever, GNSS RO measurements will be taken at two polarizations, to exploit the potential capabilities of polarimetric RO for detecting and quantifying heavy precipitation events. If the concept is proved, PAZ will mean a new application of the GNSS Radio-Occultation observations, by providing coincident thermodynamic and precipitation information with high vertical resolution within regions with thick clouds. Before the launch, a series of studies have been performed in order to assess the retrieval of precipitation information from the polarimetric observations. These studies have been based on coincident observations from the COSMIC / FORMOSAT-3 RO satellite constellation, and TRMM and GPM missions. This massive collocation exercise allowed us to build a series of Look Up Tables that relate probabilistically the precipitation intensity to the polarimetric observables. Such studies needed a previous characterization of the polarimetric observable, since it contains contributions from the ionosphere and the emitting and receiving systems. For this purpose, complete end-to-end simulations have been performed, where information from the ionospheric state

Identification of large-scale meteorological patterns associated with extreme precipitation in the US northeast

NASA Astrophysics Data System (ADS)

Agel, Laurie; Barlow, Mathew; Feldstein, Steven B.; Gutowski, William J.

2018-03-01

Patterns of daily large-scale circulation associated with Northeast US extreme precipitation are identified using both k-means clustering (KMC) and Self-Organizing Maps (SOM) applied to tropopause height. The tropopause height provides a compact representation of the upper-tropospheric potential vorticity, which is closely related to the overall evolution and intensity of weather systems. Extreme precipitation is defined as the top 1% of daily wet-day observations at 35 Northeast stations, 1979-2008. KMC is applied on extreme precipitation days only, while the SOM algorithm is applied to all days in order to place the extreme results into the overall context of patterns for all days. Six tropopause patterns are identified through KMC for extreme day precipitation: a summertime tropopause ridge, a summertime shallow trough/ridge, a summertime shallow eastern US trough, a deeper wintertime eastern US trough, and two versions of a deep cold-weather trough located across the east-central US. Thirty SOM patterns for all days are identified. Results for all days show that 6 SOM patterns account for almost half of the extreme days, although extreme precipitation occurs in all SOM patterns. The same SOM patterns associated with extreme precipitation also routinely produce non-extreme precipitation; however, on extreme precipitation days the troughs, on average, are deeper and the downstream ridges more pronounced. Analysis of other fields associated with the large-scale patterns show various degrees of anomalously strong moisture transport preceding, and upward motion during, extreme precipitation events.

Global Precipitation Measurement (GPM) Mission: Precipitation Processing System (PPS) GPM Mission Gridded Text Products Provide Surface Precipitation Retrievals

NASA Technical Reports Server (NTRS)

Stocker, Erich Franz; Kelley, O.; Kummerow, C.; Huffman, G.; Olson, W.; Kwiatkowski, J.

2015-01-01

In February 2015, the Global Precipitation Measurement (GPM) mission core satellite will complete its first year in space. The core satellite carries a conically scanning microwave imager called the GPM Microwave Imager (GMI), which also has 166 GHz and 183 GHz frequency channels. The GPM core satellite also carries a dual frequency radar (DPR) which operates at Ku frequency, similar to the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar, and a new Ka frequency. The precipitation processing system (PPS) is producing swath-based instantaneous precipitation retrievals from GMI, both radars including a dual-frequency product, and a combined GMIDPR precipitation retrieval. These level 2 products are written in the HDF5 format and have many additional parameters beyond surface precipitation that are organized into appropriate groups. While these retrieval algorithms were developed prior to launch and are not optimal, these algorithms are producing very creditable retrievals. It is appropriate for a wide group of users to have access to the GPM retrievals. However, for researchers requiring only surface precipitation, these L2 swath products can appear to be very intimidating and they certainly do contain many more variables than the average researcher needs. Some researchers desire only surface retrievals stored in a simple easily accessible format. In response, PPS has begun to produce gridded text based products that contain just the most widely used variables for each instrument (surface rainfall rate, fraction liquid, fraction convective) in a single line for each grid box that contains one or more observations.This paper will describe the gridded data products that are being produced and provide an overview of their content. Currently two types of gridded products are being produced: (1) surface precipitation retrievals from the core satellite instruments GMI, DPR, and combined GMIDPR (2) surface precipitation retrievals for the partner constellation

The Global Precipitation Patterns Associated with Short-Term Extratropical Climate Fluctuations

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.

1999-01-01

Two globally-complete, observation-only precipitation datasets have recently been developed for the Global Precipitation Climatology Project (GPCP). Both depend heavily on a variety of satellite input, as well as gauge data over land. The first, Version 2x79, provides monthly estimates on a 2.5 deg. x 2.5 deg. lat/long grid for the period 1979 through late 1999 (by the time of the conference). The second, the One-Degree Daily (1DD), provides daily estimates on a 1 deg. x l deg. grid for the period 1997 through late 1999 (by the time of the conference). Both are in beta test preparatory to release as official GPCP products. These datasets provide a unique perspective on the hydrological effects of the various atmospheric flow anomalies that have been identified by meteorologists. In this paper we discuss the regional precipitation effects that result from persistent extratropical flow anomalies. We will focus on the Pacific-North America (PNA) and North Atlantic Oscillation (NAO) patterns. Each characteristically becomes established on synoptic time scales, but then persists for periods that can exceed a month. The onset phase of each appears to have systematic mobile features, while the mature phase tend to be more stationary. Accordingly, composites of monthly data for outstanding positive and negative events (separately) contained in the 20-year record reveal the climatological structure of the precipitation during the mature phase. The climatological anomalies of the positive, negative, and (positive-negative) composites show the expected storm-track-related shifts in precipitation, and provide the advantage of putting the known precipitation effects over land in the context of the total pattern over land and ocean. As well, this global perspective points out some unexpected areas of correlation. Day-by-day composites of daily data anchored to the onset date demonstrate the systematic features during the onset. Although the 1DD has a fairly short record, some

AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

NASA Astrophysics Data System (ADS)

Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

2017-04-01

Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

Ecohydrology of dry regions of the United States: Precipitation pulses and intraseasonal drought

Treesearch

William K. Lauenroth; John B. Bradford

2009-01-01

Distribution of precipitation event sizes and interval lengths between events are important characteristics of arid and semi-arid climates. Understanding their importance will contribute to our ability to understand ecosystem dynamics in these regions. Our objective for this paper was to present a comprehensive analysis of the daily precipitation regimes of arid and...

Construction of Gridded Daily Weather Data and its Use in Central-European Agroclimatic Study

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Trnka, M.; Skalak, P.

2013-12-01

The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series, interpolated, and then modified according to the GCM- or RCM-based climate change scenarios. The present contribution, in which the parametric daily weather generator M&Rfi is linked to the high-resolution RCM output (ALADIN-Climate/CZ model) and GCM-based climate change scenarios, consists of two parts: The first part focuses on a methodology. Firstly, the gridded WG representing the baseline climate is created by merging information from observations and high resolution RCM outputs. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with RCM-simulated weather series vs. spatially scarcer observations. To represent the future climate, the WG parameters are modified according to the 'WG-friendly' climate change scenarios. These scenarios are defined in terms of changes in WG parameters and include - apart from changes in the means - changes in WG parameters, which represent the additional characteristics of the weather

Moderate to heavy cold-weather precipitation occurrences in Tehran and the associated circulation types

NASA Astrophysics Data System (ADS)

Khansalari, Sakineh; Raziei, Tayeb; Mohebalhojeh, Ali Reza; Ahmadi-Givi, Farhang

2018-02-01

Large-scale atmospheric circulations associated with 133 moderate to heavy cold-weather precipitation events recorded at Mehrabad station in Tehran, Iran, during the period 1951-2013 are analysed. To this end, the performance of un-rotated, orthogonally rotated and obliquely rotated solutions of T-mode principal component analysis (PCA) is examined in classifying the atmospheric circulations into a few representative circulation types (CTs). The T-mode PCAs were applied to the 500-hPa geopotential height for the events in a domain from 10∘E to 70∘E and from 20∘N to 50∘N. The first six leading principal components were retained and then orthogonally and obliquely rotated using varimax and promax solutions, respectively. Statistical inter-comparison of the CTs obtained using the three solutions suggests that the obliquely rotated solution is the better choice for circulation classification in the present study. The six CTs obtained using the oblique rotation were then linked to the daily total precipitation and daily mean temperature variability at Tehran station as well as to the standardized anomalies of the daily total precipitation and mean daily temperature of a dense network of stations distributed across Iran. It is found that the CTs identified, though generally comparable in producing significant precipitation in Tehran, vary in their potential to bring cold weather and generate snowfall in Tehran specifically and in the country in general. While the first three CTs give rise to regional patterns of standardized precipitation anomalies centred in Tehran, the next three CTs leave a pronounced precipitation signature almost across the whole country. As regards the standardized temperature anomalies, with the exception of one CT that causes deep and widespread negative standardized anomalies over most parts of the country, the other CTs are characterized with a dipolar structure of a deep intrusion of cold weather to the west and prevailing warm weather

Cross-Regional Assessment Of Coupling And Variability In Precipitation-Runoff Relationships

NASA Astrophysics Data System (ADS)

Carey, S. K.; Tetzlaff, D.; Soulsby, C.; Buttle, J. M.; Laudon, H.; McDonnell, J. J.; McGuire, K. J.; Seibert, J.; Shanley, J. B.

2011-12-01

The higher mid-latitudes of the northern hemisphere are particularly sensitive to change due to the important role the zero-degree isotherm plays in the phase of precipitation and intermediate storage as snow. An international inter-catchment comparison program North-Watch seeks to improve our understanding of the sensitivity of northern catchments to change by examining their hydrological and biogeochemical variability and response. The catchments are located in Sweden (Krycklan), Scotland (Mharcaidh, Girnock and Strontian), the United States (Sleepers River, Hubbard Brook and HJ Andrews) and Canada (Catamaran, Dorset and Wolf Creek). For this study, 8 catchments with 10 continuous years of daily precipitation and runoff data were selected to assess the seasonal coupling of rainfall and runoff and the memory effect of runoff events on the hydrograph at different time scales. To assess the coupling and synchroneity of precipitation, continuous wavelet transforms and wavelet coherence were used. Wavelet spectra identified the relative importance of both annual versus seasonal flows while wavelet coherence was applied to identify over different time scales along the 10-year window how well precipitation and runoff were coupled. For example, while on a given day, precipitation may be closely coupled to runoff, a wet year may not necessarily be a high runoff year in catchments with large storage. Assessing different averaging periods in the variation of daily flows highlights the importance of seasonality in runoff response and the relative influence of rain versus snowmelt on flow magnitude and variability. Wet catchments with limited seasonal precipitation variability (Strontian, Girnock) have precipitation signals more closely coupled with runoff, whereas dryer catchments dominated by snow (Wolf Creek, Krycklan) have strongly coupling only during freshet. Most catchments with highly seasonal precipitation show strong intermittent coupling during their wet season. At

PDF added value of a high resolution climate simulation for precipitation

NASA Astrophysics Data System (ADS)

Soares, Pedro M. M.; Cardoso, Rita M.

2015-04-01

General Circulation Models (GCMs) are models suitable to study the global atmospheric system, its evolution and response to changes in external forcing, namely to increasing emissions of CO2. However, the resolution of GCMs, of the order of 1o, is not sufficient to reproduce finer scale features of the atmospheric flow related to complex topography, coastal processes and boundary layer processes, and higher resolution models are needed to describe observed weather and climate. The latter are known as Regional Climate Models (RCMs) and are widely used to downscale GCMs results for many regions of the globe and are able to capture physically consistent regional and local circulations. Most of the RCMs evaluations rely on the comparison of its results with observations, either from weather stations networks or regular gridded datasets, revealing the ability of RCMs to describe local climatic properties, and assuming most of the times its higher performance in comparison with the forcing GCMs. The additional climatic details given by RCMs when compared with the results of the driving models is usually named as added value, and it's evaluation is still scarce and controversial in the literuature. Recently, some studies have proposed different methodologies to different applications and processes to characterize the added value of specific RCMs. A number of examples reveal that some RCMs do add value to GCMs in some properties or regions, and also the opposite, elighnening that RCMs may add value to GCM resuls, but improvements depend basically on the type of application, model setup, atmospheric property and location. The precipitation can be characterized by histograms of daily precipitation, or also known as probability density functions (PDFs). There are different strategies to evaluate the quality of both GCMs and RCMs in describing the precipitation PDFs when compared to observations. Here, we present a new method to measure the PDF added value obtained from

Precipitation Extremes in Dynamically Downscaled Climate Scenarios over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Shiferaw, A. S.; Tadesse, T.; Oglesby, R. J.; Rowe, C. M.

2017-12-01

The precipitation extremes were generated over the Greater Horn of Africa (GHA) using the Regional Climate Models (RCMs) simulations from the Coordinated Regional Downscaling Experiment (CORDEX). To assess how well the RCM simulations are capturing the historical observed precipitation extremes, they were compared with the precipitation extremes derived from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2). The result shows that RCM simulations have reasonably captured observed patterns of the precipitation extremes (i.e., the pattern correlation is greater than 0.5). However, significant overestimations or underestimations were observed over some localized areas in the region. The study then assessed the projected changes in these precipitation extremes during 2069-2098 and compared to the 1976-2005 period that were both derived from the RCM simulations. Projected changes in total annual precipitation (PRCPTOT), annual number of heavy (>10mm) and very heavy (>20mm) precipitation days by 2069-2098 show a general north-south pattern with a decrease over southern-half and increase over the northern-half of GHA. These changes are often greatest over parts of Somalia, Eritrea, Ethiopian highlands and southern Tanzania. Maximum 1 and 5-day total precipitation in a year and "Simple Daily Precipitation Intensity Index" (ratio of PRCPTOT to rainy days) are projected to increase over majority of GHA, including areas where PRCPTOT is projected to decrease, suggesting fewer but heavier rainy days in the future. Changes in annual sum of daily precipitation above 95th and 99th percentile are not statistically significant except Eritrea and northwestern Sudan/Somalia. Projected changes in consecutive dry days (CDD) suggest longer periods of dryness over majority of GHA. Among these areas, a substantial increases in CDD are located over southern Tanzania and Ethiopian highlands.

Multi-scale Quantitative Precipitation Forecasting Using ...

EPA Pesticide Factsheets

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals difficult to be detected at a local scale as it could cause large uncertainties when using linear correlation analysis only. This paper explores the relationship between global SST and terrestrial precipitation with respect to long-term non-stationary teleconnection signals during 1981-2010 over three regions in North America and one in Central America. Empirical mode decomposition as well as wavelet analysis is utilized to extract the intrinsic trend and the dominant oscillation of the SST and precipitation time series in sequence. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant SST regions are extracted based on the correlation coefficient. With these characterized associations, individual contribution of these SST forcing regions linked to the related precipitation responses are further quantified through nonlinear modeling with the aid of extreme learning machine. Results indicate that the non-leading SST regions also contribute a salient portion to the terrestrial precipitation variability compared to some known leading SST regions. In some cases, these

Correlation between total precipitable water and precipitation over East Asia

NASA Astrophysics Data System (ADS)

Keum, Wangho; Lim, Gyu-Ho

2017-04-01

The precipitation rate(PR) and the total precipitable water(TPW) interact with various physical mechanisms. The correlation of two variables changes with difference of domain resolution and characteristics of the region. This poster analyzes the correlation between PR and TPW over East Asia using Cyclostationary Empirical Orthogonal Function(CSEOF) which is one of the PCA analysis. The CSEOF is useful to search a periodic pattern of the data. The anomalies which is subtracted climatological mean from the original data are used to represent annual cycles. Two variances of ERA-Interim Monthly Total Column Water vapor and GPCP monthly precipitation amounts with 372 time since January, 1984 to December, 2014 are decomposed into several modes separately. The first mode which explain largest variance are used in analysis. PC of both PR and TPW increase recently on mean value and amplitude, and they show considerable correlation on phase. The correlation coefficient of PR and TPW is 0.61 and maintains the same values by month. The result of harmonic analysis shows 2 to 6 year oscillations. As result of decomposed modes of two variables, there is the relationship between TPW PC series and horizontal moisture gradient. The Horizontal moist gradient can change affect moisture flux convergence which is one of important variable of rainfall events.

Legacies of precipitation fluctuations on primary production: theory and data synthesis.

PubMed

Sala, Osvaldo E; Gherardi, Laureano A; Reichmann, Lara; Jobbágy, Esteban; Peters, Debra

2012-11-19

Variability of above-ground net primary production (ANPP) of arid to sub-humid ecosystems displays a closer association with precipitation when considered across space (based on multiyear averages for different locations) than through time (based on year-to-year change at single locations). Here, we propose a theory of controls of ANPP based on four hypotheses about legacies of wet and dry years that explains space versus time differences in ANPP-precipitation relationships. We tested the hypotheses using 16 long-term series of ANPP. We found that legacies revealed by the association of current- versus previous-year conditions through the temporal series occur across all ecosystem types from deserts to mesic grasslands. Therefore, previous-year precipitation and ANPP control a significant fraction of current-year production. We developed unified models for the controls of ANPP through space and time. The relative importance of current-versus previous-year precipitation changes along a gradient of mean annual precipitation with the importance of current-year PPT decreasing, whereas the importance of previous-year PPT remains constant as mean annual precipitation increases. Finally, our results suggest that ANPP will respond to climate-change-driven alterations in water availability and, more importantly, that the magnitude of the response will increase with time.

Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster?

NASA Astrophysics Data System (ADS)

Ban, Nikolina; Schmidli, Juerg; Schär, Christoph

2015-04-01

Climate models project that heavy precipitation events intensify with climate change. It is generally accepted that extreme day-long events will increase at a rate of about 6-7% per degree warming, consistent with the Clausius-Clapeyron relation. However, recent studies suggest that sub-daily (e.g. hourly) precipitation extremes may increase at about twice this rate (referred to as super-adiabatic scaling). Conventional climate models are not suited to assess such events, due to the limited spatial resolution and the need to parameterize convective precipitation (i.e. thunderstorms and rain showers). Here we employ a convection-resolving version of the COSMO model across an extended region (1100 km x 1100 km) covering the European Alps to investigate the differences between parameterized and explicit convection in climate-change scenarios. We conduct 10-year long integrations at resolutions of 12 and 2km. Validation using ERA-Interim driven simulations reveals major improvements with the 2km resolution, in particular regarding the diurnal cycle of mean precipitation and the representation of hourly extremes. In addition, 2km simulations replicate the observed super-adiabatic scaling at precipitation stations, i.e. peak hourly events increase faster with environmental temperature than the Clausius-Clapeyron scaling of 7%/K (see Ban et al. 2014). Convection-resolving climate change scenarios are conducted using control (1991-2000) and scenario (2081-2090) simulations driven by a CMIP5 GCM (i.e. the MPI-ESM-LR) under the IPCC RCP8.5 scenario. Consistent with previous results, projections reveal a significant decrease of mean summer precipitation (by 30%). However, unlike previous studies, we find that increase in both extreme day-long and hour-long precipitation events asymptotically intensify with the Clausius-Clapeyron relation in 2km simulation (Ban et al. 2015). Differences to previous studies might be due to the model or region considered, but we also show that

Precipitation Structure in the Sierra Nevada of California During Winter

NASA Technical Reports Server (NTRS)

Pandey, Ganesh R.; Cayan, Daniel R.; Georgakakos, Kostantine P.

1998-01-01

The influences of upper air characteristics along the coast of California upon the winter time precipitation in the Sierra Nevada region were investigated. Most precipitation episodes in the Sierra are associated with moist southwesterly winds and also tend to occur when the 700-mb temperature is close to -2 C. This favored wind direction and temperature signifies the equal importance of moisture transport and orographic lifting for maximum precipitation frequency. Making use of this observation, simple linear models were formulated to quantify the precipitation totals observed at different sites as a function of moisture transport. The skill of the model is least for daily precipitation and increases with time scale of aggregation. In terms of incremental gain, the skill of the model is optimal for an aggregation period of 5-7 days, which is also the duration of the most frequent precipitation events in the Sierra. This indicates that upper air moisture transport at can be used to make reasonable estimates of the precipitation totals for most frequent events in the Sierra region.

Investigating Soil Moisture Feedbacks on Precipitation With Tests of Granger Causality

NASA Astrophysics Data System (ADS)

Salvucci, G. D.; Saleem, J. A.; Kaufmann, R.

2002-05-01

Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(Pt;At-dt)does not equal F(P;(A-S)t-dt) where F denotes the conditional distribution of P at time t, At-dt represents the set of all knowledge available at time t-dt, and (A-S)t-dt represents all knowledge available at t-dt except S. Critical for land-atmosphere interaction research is that At-dt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed

Investigating soil moisture feedbacks on precipitation with tests of Granger causality

NASA Astrophysics Data System (ADS)

Salvucci, Guido D.; Saleem, Jennifer A.; Kaufmann, Robert

Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture ( S) feedback on precipitation ( P) using data from Illinois. In this framework S is said to Granger cause P if F(P t|Ω t- Δt )≠F(P t|Ω t- Δt -S t- Δt ) where F denotes the conditional distribution of P, Ω t- Δt represents the set of all knowledge available at time t-Δ t, and Ω t- Δt -S t- Δt represents all knowledge except S. Critical for land-atmosphere interaction research is that Ω t- Δt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed

A multimodel intercomparison of resolution effects on precipitation: simulations and theory

NASA Astrophysics Data System (ADS)

Rauscher, Sara A.; O'Brien, Travis A.; Piani, Claudio; Coppola, Erika; Giorgi, Filippo; Collins, William D.; Lawston, Patricia M.

2016-10-01

An ensemble of six pairs of RCM experiments performed at 25 and 50 km for the period 1961-2000 over a large European domain is examined in order to evaluate the effects of resolution on the simulation of daily precipitation statistics. Application of the non-parametric two-sample Kolmorgorov-Smirnov test, which tests for differences in the location and shape of the probability distributions of two samples, shows that the distribution of daily precipitation differs between the pairs of simulations over most land areas in both summer and winter, with the strongest signal over southern Europe. Two-dimensional histograms reveal that precipitation intensity increases with resolution over almost the entire domain in both winter and summer. In addition, the 25 km simulations have more dry days than the 50 km simulations. The increase in dry days with resolution is indicative of an improvement in model performance at higher resolution, while the more intense precipitation exceeds observed values. The systematic increase in precipitation extremes with resolution across all models suggests that this response is fundamental to model formulation. Simple theoretical arguments suggest that fluid continuity, combined with the emergent scaling properties of the horizontal wind field, results in an increase in resolved vertical transport as grid spacing decreases. This increase in resolution-dependent vertical mass flux then drives an intensification of convergence and resolvable-scale precipitation as grid spacing decreases. This theoretical result could help explain the increasingly, and often anomalously, large stratiform contribution to total rainfall observed with increasing resolution in many regional and global models.

Application of Clustering Algorithms to Observed and Simulated Daily Precipitation over the Tropical and Southern Pacific Ocean

NASA Astrophysics Data System (ADS)

Pike, M.; Lintner, B. R.

2017-12-01

We apply two data organization methods, self-organizing maps (SOMs) and k-means clustering with linear unidimensional scaling (k-means+LUS), to identify and organize the spatial patterns inherent in daily austral summer (December-January-February or DJF) rainfall over the tropical and southern Pacific Ocean basins from Tropical Rainfall Measuring Mission (TRMM) satellite observations. For either a 2x2 SOM or k = 4 clustering of all available DJFs from 1998-2013, we find an El Niño/Southern Oscillation (ENSO) signature, with pairs of maps reflecting either El Niño or La Niña phase conditions. Within each of the ENSO-phase pairs, one map favors Intertropical Convergence Zone (ITCZ)-active conditions, in which precipitation is more intense over the ITCZ region compared to the South Pacific Convergence Zone (SPCZ) region, while the remaining one is SPCZ-active. The SPCZ-active maps show a spatial translation of the principal SPCZ diagonal consistent with the impacts of El Niño/Southern Oscillation (ENSO) or analogous low-frequency modes of variability on the SPCZ as shown in prior studies. Because of the dominant impact of ENSO, we further apply these methods separately on subsets of rainfall data for each ENSO phase. While the overall position of the SPCZ is sensitive to the phase of ENSO, within each phase, more- or less-steeply sloped SPCZ diagonals may occur. Thus, while the mean position of the SPCZ is largely controlled by ENSO phase, the distinct orientations of the SPCZ within the same ENSO phase point to higher-frequency modulation of SPCZ slope. To investigate the nature of these further, we construct composites of pressure-level winds and specific humidity from the Climate Forecast System Reanalysis (CFSR) associated with the rainfall patterns. For either SOM or kmeans-based composites, we find large-scale dynamics and moisture signatures that are consistent with the rainfall patterns and which we interpret in terms of previously described mechanisms of

Simulation of precipitation by weather pattern and frontal analysis

NASA Astrophysics Data System (ADS)

Wilby, Robert

1995-12-01

Daily rainfall from two sites in central and southern England was stratified according to the presence or absence of weather fronts and then cross-tabulated with the prevailing Lamb Weather Type (LWT). A semi-Markov chain model was developed for simulating daily sequences of LWTs from matrices of transition probabilities between weather types for the British Isles 1970-1990. Daily and annual rainfall distributions were then simulated from the prevailing LWTs using historic conditional probabilities for precipitation occurrence and frontal frequencies. When compared with a conventional rainfall generator the frontal model produced improved estimates of the overall size distribution of daily rainfall amounts and in particular the incidence of low-frequency high-magnitude totals. Further research is required to establish the contribution of individual frontal sub-classes to daily rainfall totals and of long-term fluctuations in frontal frequencies to conditional probabilities.

Observed Trends in Indices of Daily Precipitation and Temperature Extremes in Rio de Janeiro State (brazil)

NASA Astrophysics Data System (ADS)

Silva, W. L.; Dereczynski, C. P.; Cavalcanti, I. F.

2013-05-01

One of the main concerns of contemporary society regarding prevailing climate change is related to possible changes in the frequency and intensity of extreme events. Strong heat and cold waves, droughts, severe floods, and other climatic extremes have been of great interest to researchers because of its huge impact on the environment and population, causing high monetary damages and, in some cases, loss of life. The frequency and intensity of extreme events associated with precipitation and air temperature have been increased in several regions of the planet in recent years. These changes produce serious impacts on human activities such as agriculture, health, urban planning and development and management of water resources. In this paper, we analyze the trends in indices of climatic extremes related to daily precipitation and maximum and minimum temperatures at 22 meteorological stations of the National Institute of Meteorology (INMET) in Rio de Janeiro State (Brazil) in the last 50 years. The present trends are evaluated using the software RClimdex (Canadian Meteorological Service) and are also subjected to statistical tests. Preliminary results indicate that periods of drought are getting longer in Rio de Janeiro State, except in the North/Northwest area. In "Vale do Paraíba", "Região Serrana" and "Região dos Lagos" the increase of consecutive dry days is statistically significant. However, we also detected an increase in the total annual rainfall all over the State (taxes varying from +2 to +8 mm/year), which are statistically significant at "Região Serrana". Moreover, the intensity of heavy rainfall is also growing in most of Rio de Janeiro, except in "Costa Verde". The trends of heavy rainfall indices show significant increase in the "Metropolitan Region" and in "Região Serrana", factor that increases the vulnerability to natural disasters in these areas. With respect to temperature, it is found that the frequency of hot (cold) days and nights is

Annual and seasonal analysis of temperature and precipitation in Andorra (Pyrenees) from 1934 to 2008: quality check, homogenization and trends

NASA Astrophysics Data System (ADS)

Esteban, Pere; Prohom, Marc; Aguilar, Enric; Mestre, Olivier

2010-05-01

The analysis of temperature and precipitation change and variability in high elevations is a difficult issue due to the lack of long term climatic series in those environments. Nonetheless, it is important to evaluate how much high elevations follow the same climate evolution than low lying sites. In this work, using daily data from three Andorran weather stations (maintained by the power company Forces Elèctriques d'Andorra, FEDA), climate trends of annual and seasonal temperature and precipitation were obtained for the period 1934-2008. The series are complete (99.9%) and are located in a mountainous area ranging from 1110 m to 1600 m asl. As a previous step to the analysis, data rescue, quality control and homogeneity tests were applied to the daily data. For quality control, several procedures were applied to identify and flag suspicious or erroneous data: duplicated days, outliers, excessive differences between consecutive days, flat line checking, days with maximum temperature lower that minimum temperature, and rounding analysis. All the station sites were visited to gather the available metadata. Concerning homogeneity, a homogeneous climate time series is defined as one where variations are caused only by variations in climate and not to non-climatic factors (i.e., changes in site location, instruments, station environment…). As a result, homogeneity of the series was inspected from several methodologies that have been used in a complementary and independent way in order to attain solid results: C3-SNHT (with software developed under the Spanish Government Grant CGL2007-65546-C03-02), and Caussinus-Mestre (C-M) approaches. In both cases, tests were applied to mean annual temperature and precipitation series, using Catalan and French series as references (provided respectively by the Meteorological Service of Catalonia and Météo-France, in the framework of the Action COST-ES0601: Advances in homogenisation methods of climate series: an integrated

Landslide triggering thresholds for Switzerland based on a new gridded precipitation dataset

NASA Astrophysics Data System (ADS)

Leonarduzzi, Elena; Molnar, Peter; McArdell, Brian W.

2017-04-01

In Switzerland floods are responsible for most of the damage caused by rainfall-triggered natural hazards (89%), followed by landslides (6%, ca. 520 M Euros) as reported in Hilker et al. (2009) for the period 1972-2007. The prediction of landslide occurrence is particularly challenging because of their wide distribution in space and the complex interdependence of predisposing and triggering factors. The overall goal of our research is to develop an Early Warning System for landsliding in Switzerland based on hydrological modelling and rainfall forecasts. In order to achieve this, we first analyzed rainfall triggering thresholds for landslides from a new gridded daily precipitation dataset (RhiresD, MeteoSwiss) for Switzerland combined with landslide events recorded in the Swiss Damage Database (Hilker et al.,2009). The high-resolution gridded precipitation dataset allows us to collocate rainfall and landslides accurately in space, which is an advantage over many previous studies. Each of the 2272 landslides in the database in the period 1972-2012 was assigned to the corresponding 2x2 km precipitation cell. For each of these cells, precipitation events were defined as series of consecutive rainy days and the following event parameters were computed: duration (day), maximum and mean daily intensity (mm/day), total rainfall depth (mm) and maximum daily intensity divided by Mean Daily Precipitation (MDP). The events were classified as triggering or non-triggering depending on whether a landslide was recorded in the cell during the event. This classification of observations was compared to predictions based on a threshold for each of the parameters. The predictive power of each parameter and the best threshold value were quantified by ROC analysis and statistics such as AUC and the True Skill Statistic (TSS). Event parameters based on rainfall intensity were found to have similarly high predictive power (TSS=0.54-0.59, AUC=0.85-0.86), while rainfall duration had a

Horsing Around with Climate: Effect of Technology-Driven Landuse Change on Extreme Precipitation

NASA Astrophysics Data System (ADS)

Sines, T. R.; Arritt, R. W.

2016-12-01

The shift from work animals such as horses to mechanized labor and transport led to a decrease in acreage devoted to small grains (primarily oats) in the United States. Land formerly devoted to these crops was converted mostly to soybeans, which saw a forty-fold increase in planted acreage from 1940 to present. The same period saw an increase in extreme precipitation over the continental United States. We investigate possible connections between this agricultural landuse modification and precipitation changes in the central United States using the WRF-ARW model coupled with the Community Land Model. Crop acreages for maize, soybean, winter wheat, spring wheat, and other C3 and C4 crops were reconstructed for 1940-2010 using county-level data. This landuse was then used as surface input for two regional climate simulations, one using constant 1940s landuse and another using constant 2010 landuse. The landuse change was found to produce a shift in the precipitation intensity spectrum, with simulations using 2010 landuse having higher frequencies for heavier precipitation amounts and lower frequencies of light amounts compared to 1940s landuse. The break point for this shift corresponded to daily precipitation of about 24 mm. This indicates that agricultural landuse change has contributed to the observed trend in extreme precipitation, increasing the frequency of heavy daily rainfall.

Effects of Extreme Precipitation to the Distribution of Infectious Diseases in Taiwan, 1994–2008

PubMed Central

Chen, Mu-Jean; Lin, Chuan-Yao; Wu, Yi-Ting; Wu, Pei-Chih; Lung, Shih-Chun; Su, Huey-Jen

2012-01-01

The incidence of extreme precipitation has increased with the exacerbation of worldwide climate disruption. We hypothesize an association between precipitation and the distribution patterns that would affect the endemic burden of 8 infectious diseases in Taiwan, including water- and vector-borne infectious diseases. A database integrating daily precipitation and temperature, along with the infectious disease case registry for all 352 townships in the main island of Taiwan was analysed for the period from 1994 to 2008. Four precipitation levels, <130 mm, 130–200 mm, 200–350 mm and >350 mm, were categorized to represent quantitative differences, and their associations with each specific disease was investigated using the Generalized Additive Mixed Model and afterwards mapped on to the Geographical Information System. Daily precipitation levels were significantly correlated with all 8 mandatory-notified infectious diseases in Taiwan. For water-borne infections, extreme torrential precipitation (>350 mm/day) was found to result in the highest relative risk for bacillary dysentery and enterovirus infections when compared to ordinary rain (<130 mm/day). Yet, for vector-borne diseases, the relative risk of dengue fever and Japanese encephalitis increased with greater precipitation only up to 350 mm. Differential lag effects following precipitation were statistically associated with increased risk for contracting individual infectious diseases. This study’s findings can help health resource sector management better allocate medical resources and be better prepared to deal with infectious disease outbreaks following future extreme precipitation events. PMID:22737206

THE EFFECT OF SALICYLATES ON THE PRECIPITATION OF ANTIGEN WITH ANTIBODY.

PubMed

Coburn, A F; Kapp, E M

1943-02-01

1. Sodium salicylate modifies the precipitation of normal rabbit serum protein by sodium tungstate, and partially inhibits the precipitation of horse serum euglobulin by rabbit antiserum. Sodium salicylate added to a system containing crystalline egg albumin and its antibody partly prevents the formation of precipitate, the degree of inhibition being related to the concentration of salicylate. 2. Precipitation in the equivalence zone is more readily prevented by salicylate than precipitation in the region of antibody excess, the immune system becoming progressively less sensitive to the action of salicylate as the excess of antibody becomes larger. 3. Formed precipitates were partly dissolved following resuspension in the presence of salicylate. 4. The salicylate effect on immune precipitation is reversible, and appears to be due to inactivation of antibody. 5. Salicylate was more effective in preventing specific precipitation than other anions of a lyotropic series tested.

A procedure for assessing future trends of subdaily precipitation values on point scale

NASA Astrophysics Data System (ADS)

Rianna, Guido; Villani, Veronica; Mercogliano, Paola; Vezzoli, Renata

2015-04-01

In many areas of Italy, urban flooding or floods in small mountain basins, induced by heavy precipitations on subdaily scale, represent remarkable hazards able to cause huge damages and casualties often increased by very high population density. A proper assessment about how frequency and magnitude of such events could change under the effect of Climate Changes (CC) is crucial for the development of future territorial planning (such as early warning systems). The current constraints of climate modeling, also using high resolution RCM, prevent an adequate representation of subdaily precipitation patterns (mainly concerning extreme values) while available observed datasets are often unsuitable for the application of the bias-correction (BC) techniques requiring long time series. In this work, a new procedure is proposed: at point scale, precipitation outputs on 24 and 48 hours are provided by high resolution (about 8km) climate simulation performed through the RCM COSMO_CLM driven by GCM CMCC_CM and bias-corrected by quantile mapping approach. These ones are adopted for a monthly stochastic disaggregation approach combining Random Parameter Bartlett-Lewis (RPBL) gamma model with appropriate rainfall disaggregation technique. The last one implements empirical correction procedures, called adjusting procedures, to modify the model rainfall output, so that it is consistent with the observed rainfall values on daily time scale. In order to take into account the great difficulties related to minimization of objective function required by retrieving the 7 RPBL parameters, for each dataset the computations are repeated twenty times. Moreover, adopting statistical properties on 24 and 48 hours to retrieve RPBL parameters allows, according Bo et al. (1994), to infer statistical properties until hourly scale maintaining the information content about the possible changes in precipitation patterns due to CC. The entire simulation chain is tested on Baiso weather station, in

Long-term variability and changes in thunderstorm induced extreme precipitation in Slovakia over 1951-2010

NASA Astrophysics Data System (ADS)

Pecho, J.; Faško, P.; Bližák, V.; Kajaba, P.; Košálová, J.; Bochníček, O.; Lešková, L.

2012-04-01

It is well known that extreme precipitation associated with intensive rains, in summer induced mostly by local thunderstorm activity, could cause very significant problems in economical and social spheres of the countries. Heavy precipitation and consecutive flash-floods are the most serious weather-related hazards over the territory of Slovakia. The extreme precipitation analyses play a strategic role in many climatological and hydrological evaluations designed for the wide range of technical and engineering applications as well as climate change impact assessments. A thunderstorm, as a violent local storm produced by a cumulonimbus cloud and accompanied by thunder and lightning, represents extreme convective activity in the atmosphere depending upon the release of latent heat, by the condensation of water vapor, for most of its energy. Under the natural conditions of Slovakia the incidence of thunderstorms has been traditionally concentrated in the summer or warm half-year (Apr.-Sept.), but increasing air temperature resulting in higher water vapor content and more intense short-term precipitation is associated with more frequent thunderstorm occurrence in early spring as well as autumn. It is the main reason why the studies of thunderstorm phenomena have increased in Slovakia in recent years. It was found that thunderstorm occurrence, in terms of incidence of storm days, has profoundly changed particularly in spring season (~ 30 % in April and May). The present contribution is devoted to verifying the hypothesis that recently the precipitation has been more intense and significant shifts in seasonal incidence have occurred in particular regions in Slovakia. On the basis of the 60-year (1951-2010) meteorological observation series obtained from more than 20 synoptic stations, the analysis of trends and long-term variability of the days with thunderstorms and the accompanying precipitation for seasons was undertaken. Contribution also attempts to explain the main

One-day offset in daily hydrologic modeling: An exploration of the issue in automatic model calibration

USDA-ARS?s Scientific Manuscript database

The literature of daily hydrologic modelling illustrates that daily simulation models are incapable of accurately representing hydrograph timing due to relationships between precipitation and watershed hydrologic response. For watersheds with a time of concentration less than 24 hrs and a late day p...

Bias correction of precipitation data and its effects on aridity and drought assessment in China over 1961-2015.

PubMed

Yao, Ning; Li, Yi; Li, Na; Yang, Daqing; Ayantobo, Olusola Olaitan

2018-10-15

The accuracy of gauge-measured precipitation (P m ) affects drought assessment since drought severity changes due to precipitation bias correction. This research investigates how drought severity changes as the result of bias-corrected precipitation (P c ) using the Erinc's index I m and standardized precipitation evapotranspiration index (SPEI). Daily and monthly P c values at 552 sites in China were determined using daily P m and wind speed and air temperature data over 1961-2015. P c -based I m values were generally larger than P m -based I m for most sub-regions in China. The increased P c and P c -based I m values indicated wetter climate conditions than previously reported for China. After precipitation bias-correction, Climate types changed, e.g., 20 sites from severe-arid to arid, and 11 sites from arid to semi-arid. However, the changes in SPEI were not that obvious due to precipitation bias correction because the standardized index SPEI removed the effects of mean precipitation values. In conclusion, precipitation bias in different sub-regions of China changed the spatial and temporal characteristics of drought assessment. Copyright © 2018 Elsevier B.V. All rights reserved.