scholarly journals Topoclimatic modeling for minimum temperature prediction at a regional scale in the Central Valley of Chile*

Agronomie ◽  
1997 ◽  
Vol 17 (6-7) ◽  
pp. 307-314 ◽  
Author(s):  
F. Santibáñez ◽  
L. Morales ◽  
J. de la Fuente ◽  
P. Cellier ◽  
A. Huete
Keyword(s):  
Minimum Temperature ◽  
Regional Scale ◽  
Central Valley ◽  
Temperature Prediction

scholarly journals Temperature Trend Analysis and Extreme High Temperature Prediction Based on Weighted Markov Model in Lanzhou

2021 ◽  
Author(s):  
Zhiqiang Pang ◽  
Zhaoxu Wang
Keyword(s):  
High Temperature ◽  
Markov Model ◽  
Prediction Model ◽  
Trend Analysis ◽  
High Temperatures ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Temperature Prediction ◽  
Real Situation ◽  
Change Trend

Abstract In this study, temporIn this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high-temperature prediction was constructed. Several non-parametric methods were used to analyze the time trend. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature, and average temperature in Lanzhou have a significant rising trend and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation. and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation.

scholarly journals A Statistical Framework to Identify the Influence of Large-Scale Weather Events on Regional Air Pollution

2011 ◽  
Vol 50 (12) ◽  
pp. 2376-2393 ◽  
Author(s):  
Angadh Singh ◽  
Ahmet Palazoglu
Keyword(s):  
Air Pollution ◽  
Large Scale ◽  
Regional Scale ◽  
Central Valley ◽  
Temporal Patterns ◽  
Pollution Levels ◽  
Statistical Framework ◽  
Weather Events ◽  
The Pacific ◽  
Regional Air Pollution

AbstractRegional air pollution episodes occur as a result of increased emissions and prevalence of conducive meteorological conditions. The frequency of occurrence of such favorable conditions on a regional scale may be influenced by large-scale climatic events like ENSO and the Pacific decadal oscillation (PDO). The scarcity of measurements of criteria pollutants, especially ozone and particulate matter (PM), prior to the last 10–15-yr period, limits the scope of observing the influence of climate variability during recent decades on regional pollution levels. The authors propose a novel statistical framework to utilize available measurements and characterize synoptic influences on regional PM pollution in California’s Central Valley during 1998–2008. The identified target conditions are used to develop a classification scheme to scan historical climate datasets dating back to 1948. The procedure identifies exceedance-conducive days during 1950–98, when no PM2.5 measurements were available. Temporal patterns in seasonal frequency of these identified exceedance-conducive days are investigated for temporal patterns driven by ENSO and PDO.

scholarly journals Examining regional groundwater–surface water dynamics using an integrated hydrologic model of the San Joaquin River basin

2017 ◽  
Vol 21 (2) ◽  
pp. 923-947 ◽  
Author(s):  
James M. Gilbert ◽  
Reed M. Maxwell
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Regional Scale ◽  
Power Spectra ◽  
Central Valley ◽  
Hydrologic Model ◽  
Water Dynamics ◽  
Irrigated Agriculture ◽  
San Joaquin River ◽  
Basin Scale

Abstract. Widespread irrigated agriculture and a growing population depend on the complex hydrology of the San Joaquin River basin in California. The challenge of managing this complex hydrology hinges, in part, on understanding and quantifying how processes interact to support the groundwater and surface water systems. Here, we use the integrated hydrologic platform ParFlow-CLM to simulate hourly 1 km gridded hydrology over 1 year to study un-impacted groundwater–surface water dynamics in the basin. Comparisons of simulated results to observations show the model accurately captures important regional-scale partitioning of water among streamflow, evapotranspiration (ET), snow, and subsurface storage. Analysis of this simulated Central Valley groundwater system reveals the seasonal cycle of recharge and discharge as well as the role of the small but temporally constant portion of groundwater recharge that comes from the mountain block. Considering uncertainty in mountain block hydraulic conductivity, model results suggest this component accounts for 7–23 % of total Central Valley recharge. A simulated surface water budget guides a hydrograph decomposition that quantifies the temporally variable contribution of local runoff, valley rim inflows, storage, and groundwater to streamflow across the Central Valley. Power spectra of hydrograph components suggest interactions with groundwater across the valley act to increase longer-term correlation in San Joaquin River outflows. Finally, model results reveal hysteresis in the relationship between basin streamflow and groundwater contributions to flow. Using hourly model results, we interpret the hysteretic cycle to be a result of daily-scale fluctuations from precipitation and ET superimposed on seasonal and basin-scale recharge and discharge.

Composite Predictor Maps of Extraordinary Weather Events in the Sacramento, California, Region*

2008 ◽  
Vol 23 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Richard Grotjahn ◽  
Ghislain Faure
Keyword(s):  
Heat Waves ◽  
Statistical Significance ◽  
Regional Scale ◽  
Central Valley ◽  
Significance Test ◽  
Theoretical Concept ◽  
Thermal Fields ◽  
Weather Events ◽  
Composite Maps ◽  
Rain Events

Abstract Extraordinary weather events in the Sacramento, California, region are examined using a simple compositing technique. The extraordinary events identified are uncommon and the worst of their kind, but not necessarily severe. While the criteria outlined herein are drawn from Sacramento weather station data, the identified events are extraordinary elsewhere over much, if not all, of California’s Central Valley. Several types of extraordinary events are highlighted, including the hardest freezes, heaviest prolonged rain events, longest-duration fog, and worst heat waves (onset and end) in a 21-yr period. Bootstrap resampling establishes the statistical significance of features on the composite maps. The composite maps with statistically significant features highlighted allow a forecaster to search for key features in forecast maps that coexist with or that precede an extraordinary weather event. Local- and regional-scale extraordinary events have larger-scale signatures that can be traced back in time. Many of these features are intuitive and known to local forecasters (and that provides a check upon the methodology used here). However, some features appear to be unexpected. For example, a ridge (in height and thermal fields) over the southeastern United States generally occurs prior to the worst heat waves and hardest freezes. Some features appear to exhibit the theoretical concept of downstream development. Several extraordinary weather types are preceded by a ridge either over Alaska (hardest freezes and heaviest prolonged rain) or just west of Alaska (worst heat waves). While the Alaskan ridge passes a significance test, the presence of other features (such as the southeastern ridge) determines what, if any, extraordinary event occurs near Sacramento. However, a feature that passes the significance test for the composite might not occur in every member of a given extraordinary event. The height and thermal patterns over the West Coast and North Pacific are similar for summer’s worst heat waves and winter’s longest-duration fog: both types of events are preceded by a trough in the eastern mid-Pacific.

scholarly journals Maximum and minimum temperature prediction over western Himalaya using artificial neural network

MAUSAM ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 283-290
Author(s):  
PIYUSH JOSHI ◽  
A. GANJU
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Minimum Temperature ◽  
Linear Method ◽  
Western Himalaya ◽  
Temperature Prediction ◽  
Data Set ◽  
Non Linear ◽  
Artificial Neural ◽  
Mean Square Errors

Due to eastward moving synoptic weather system called Western Disturbance (WD), Western Himalaya receives enormous amount of precipitation in the form of snow during winter months (November to April). This precipitation keeps on accumulating and poses an avalanche threat. Temperature plays an important role for the initiation of avalanches. Therefore, prediction of maximum and minimum temperature may be quite helpful for avalanche forecasting. In the present study Artificial Neural Network (ANN), a non-linear method is used for the prediction of maximum and minimum temperature using surface meteorological data observed at various observatories in Western Himalaya region. ANN provides a computational efficient way of determining an empirical possible non-linear relationship between a number of input and one or more outputs. In present study back propagation learning algorithm is used to train the network. In the training process the relationship between input and output is extracted i.e., final weights are computed. Past data of about 25 years is used for training the network and trained network is used for temperature prediction for five winter seasons (2005-06 to 2009-10). Root mean square errors (RMSE) corresponding to maximum and minimum temperature are computed. For independent data set RMSE vary from 2.18 to 2.48 and 1.99 to 2.78 for maximum and minimum temperatures respectively.

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