Probabilistic Forecasts from the National Digital Forecast Database

2008 ◽  
Vol 23 (2) ◽  
pp. 270-289 ◽  
Author(s):  
Roman Krzysztofowicz ◽  
W. Britt Evans
Keyword(s):  
Meteorological Data ◽  
Gaussian Model ◽  
Distribution Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Point Estimate ◽  
Daily Maximum ◽  
Climatic Data ◽  
Estimation Of Parameters ◽  
Probabilistic Forecasts

Abstract The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, and a quantile function). The quantification of uncertainty is accomplished via Bayes theorem by extracting and fusing two kinds of information from two different sources: (i) a long sample of the predictand from the National Climatic Data Center, and (ii) a short sample of the official National Weather Service forecast from the National Digital Forecast Database. The official forecast is deterministic and hence deficient: it contains no information about uncertainty. The BPF remedies this deficiency by outputting the complete and well-calibrated characterization of uncertainty needed by decision makers and information providers. The BPF comes furnished with (i) the meta-Gaussian model, which fits meteorological data well as it allows all forms of marginal distribution functions, and nonlinear and heteroscedastic dependence structures, and (ii) the statistical procedures for estimation of parameters from asymmetric samples and for coping with nonstationarities in the predictand and the forecast due to the annual cycle and the lead time. A comprehensive illustration of the BPF is reported for forecasts of the daily maximum temperature issued with lead times of 1, 4, and 7 days for three stations in two seasons (cool and warm).

scholarly journals A Statistical Adjustment of Regional Climate Model Outputs to Local Scales: Application to Platja de Palma, Spain

2012 ◽  
Vol 25 (3) ◽  
pp. 939-957 ◽  
Author(s):  
A. Amengual ◽  
V. Homar ◽  
R. Romero ◽  
S. Alonso ◽  
C. Ramis
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Signal ◽  
Daily Maximum ◽  
Climate Projections ◽  
Recent Past

Abstract Projections of climate change effects for the System of Platja de Palma (SPdP) are derived using a novel statistical technique. Socioeconomic activities developed in this settlement are very closely linked to its climate. Any planning for socioeconomic opportunities in the mid- and long term must take into account the possible effects of climate change. To this aim, daily observed series of minimum and maximum temperatures, precipitation, relative humidity, cloud cover, and wind speed have been analyzed. For the climate projections, daily data generated by an ensemble of regional climate models (RCMs) have been used. To properly use RCM data at local scale, a quantile–quantile adjustment has been applied to the simulated regional projections. The method is based on detecting changes in the cumulative distribution functions between the recent past and successive time slices of the simulated climate and applying these, after calibration, to the recent past (observed) series. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs, although the correction does not result in such clear improvement when dealing with very extreme rainfalls. Next, the corrected series are analyzed to quantify the climate change signal. An increase of the annual means for temperatures together with a decrease for the remaining variables is projected throughout the twenty-first century. Increases in weak and intense daily rainfalls and in high extremes for daily maximum temperature can also be expected. With this information at hand, the experts planning the future of SPdP can respond more effectively to the problem of local adaptation to climate change.

scholarly journals Meteorological factors affecting the risk of transmission of HPAI in Miyazaki, Japan

2019 ◽  
Vol 6 (1) ◽  
pp. e000341 ◽  
Author(s):  
Genki Arikawa ◽  
Yoshinori Fujii ◽  
Maiku Abe ◽  
Ngan Thi Mai ◽  
Shuya Mitoma ◽  
...  
Keyword(s):  
Bird Migration ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Migratory Waterfowl ◽  
Generalised Linear Mixed Model ◽  
Fulica Atra ◽  
The Relationship

Highly pathogenic avian influenza (HPAI) outbreaks engender a severe economic impact on the poultry industry and public health. Migratory waterfowl are considered the natural hosts of HPAI virus, and HPAI viruses are known to be transmitted over long distances during seasonal bird migration. Bird migration is greatly affected by the weather. Many studies have shown the relationship between either autumn or spring bird migration and climate. However, few studies have shown the relationship between annual bird migration and annual weather. This study aimed to establish a model for the number of migratory waterfowl involved in HPAI virus transmission based on meteorological data. From 136 species of waterfowl that were observed at Futatsudate in Miyazaki, Japan, from 2008 to 2016, we selected potential high-risk species that could introduce the HPAI virus into Miyazaki and defined them as ‘risky birds’. We also performed cluster analysis to select meteorological factors. We then analysed the meteorological data and the total number of risky birds using a generalised linear mixed model. We selected 10 species as risky birds: Mallard (Anas platyrhynchos), Northern pintail (Anas acuta), Eurasian wigeon (Anas penelope), Eurasian teal (Anas crecca), Common pochard (Aythya ferina), Eurasian coot (Fulica atra), Northern shoveler (Anas clypeata), Common shelduck (Tadorna tadorna), Tufted duck (Aythya fuligula) and Herring gull (Larus argentatus). We succeeded in clustering 35 meteorological factors into four clusters and identified three meteorological factors associated with their migration: (1) the average daily maximum temperature; (2) the mean value of global solar radiation and (3) the maximum daily precipitation. We thus demonstrated the relationship between the number of risky birds and meteorological data. The dynamics of migratory waterfowl was relevant to the risk of an HPAI outbreak, and our data could contribute to cost and time savings in strengthening preventive measures against epidemics.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

scholarly journals Relationship between several meteorological factors and phenological features of apple cultivars

2008 ◽  
Vol 14 (1-2.) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
S. Zhongfu ◽  
Y. Wang ◽  
J. Racskó ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Apple Cultivars

The trees observed are grown at Ofeherto, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple cultivars. Each of the cultivars were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001. during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming cultivars start blooming at 10-21April. The cultivars of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2-10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor influencing the start of bloom in the subsequent spring.

scholarly journals Observational Evidences of Double Cropping Impacts on the Climate in the Northern China Plains

2012 ◽  
Vol 25 (13) ◽  
pp. 4721-4728 ◽  
Author(s):  
C.-H. Ho ◽  
S.-J. Park ◽  
S.-J. Jeong ◽  
J. Kim ◽  
J.-G. Jhun
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Regional Climate ◽  
Northern China ◽  
Specific Humidity ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Double Cropping

Abstract The impacts of harvested cropland in the double cropping region (DCR) of the northern China plains (NCP) on the regional climate are examined using surface meteorological data and the satellite-derived normalized difference vegetation index (NDVI) and land surface temperature (LST). The NDVI data are used to distinguish the DCR from the single cropping region (SCR) in the NCP. Notable increases in LST in the period May–June are found in the area identified as the DCR on the basis of the NDVI data. The difference between the mean daily maximum temperature averaged over the DCR and SCR stations peaks at 1.27°C in June. The specific humidity in the DCR is significantly smaller than in the SCR. These results suggest that the enhanced agricultural production by multiple cropping may amplify regional warming and aridity to further modify the regional climate in addition to the global climate change. Results in this study may also be used as a quantitative observed reference state of the crop/vegetation effects for future climate modeling studies.

scholarly journals Impacts of Some Climatic Variables on the Seasonal Productivity of Aman Rice at Dhaka Region, Central Part of Bangladesh

2016 ◽  
Vol 8 (2) ◽  
pp. 7-10
Author(s):  
SMSA Tuhin ◽  
MA Farukh ◽  
BS Nahar ◽  
MA Baten
Keyword(s):  
Crop Production ◽  
Climatic Variability ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Average Maximum ◽  
Average Maximum Temperature ◽  
Daily Sunshine

An agro-climatic study was conducted at Dhaka region of Bangladesh using 43 years (1970-2012) of climatic data (daily maximum temperature, seasonal total rainfall, daily average humidity, and daily sunshine hour) to observe the climatic variability and their impacts on the productivity of Aman rice. The average maximum temperature increased by 0.04°C in Aman season in Dhaka region. The average sunshine hours decreased by 0.05 in the season. The average humidity decreased by 0.14% in the season. The average seasonal rainfall increased slightly by 0.09 mm in the season. The Aman rice production increased by 0.03 t ha-1 in the region. The production year 2003 shows highest productivity due to less climatic devastation impact on the seasonal productivity of the rice. The climatic variables impact ( Savg > Havg > Tmax ) implies the seasonal productivity of Aman rice was mostly and inversely correlated with average sunshine (Savg) hour. However, most of the time the production showed increasing trend except some devastating natural calamities in the year of 1988 and 1998 which affected crop production seriously.J. Environ. Sci. & Natural Resources, 8(2): 7-10 2015

scholarly journals Effects of weather characteristics on blooming dates in an apple gene bank plantation between 1984 and 2001

2006 ◽  
Vol 12 (2) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
J. Racskó ◽  
M. Soltész ◽  
Z. Szabó ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Daily Precipitation ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Weather Characteristics

The aims of this paper was to investigate the flowering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Ujfehert6, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001. During this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10-21 April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2-10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor influencing the start of bloom in the subsequent spring.

scholarly journals Relationship between several meteorological factors and phenological features of pear cultivars

2016 ◽  
Vol 18 (2) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
G. Kocsisné Molnár ◽  
J. Racskó ◽  
M. Soltész ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time

The aim of this paper was to investigate the fl owering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Újfehértó, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001 during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10–21April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2–10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor infl uencing the start of bloom in the subsequent spring.

scholarly journals Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily J. Wilkins ◽  
Peter D. Howe ◽  
Jordan W. Smith
Keyword(s):  
National Park ◽  
National Park Service ◽  
Spatial Behavior ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Weather ◽  
Visitor Behavior ◽  
Park Service ◽  
The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

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