Near surface air temperature lapse rates over complex terrain: a WRF based analysis of controlling factors and processes for the central Himalayas

2019 ◽  
Vol 54 (1-2) ◽  
pp. 329-349 ◽  
Author(s):  
Ramchandra Karki ◽  
Shabeh ul Hasson ◽  
Udo Schickhoff ◽  
Thomas Scholten ◽  
Jürgen Böhner ◽  
...  
2017 ◽  
Vol 131 (3-4) ◽  
pp. 1221-1234 ◽  
Author(s):  
Mingxia Du ◽  
Mingjun Zhang ◽  
Shengjie Wang ◽  
Xiaofan Zhu ◽  
Yanjun Che

2020 ◽  
Vol 66 (257) ◽  
pp. 386-400
Author(s):  
Patrick Troxler ◽  
Álvaro Ayala ◽  
Thomas E. Shaw ◽  
Matt Nolan ◽  
Ben W. Brock ◽  
...  

AbstractWe examine the spatial patterns of near-surface air temperature (Ta) over a melting glacier using a multi-annual dataset from McCall Glacier, Alaska. The dataset consists of a 10-year (2005–2014) meteorological record along the glacier centreline up to an upper glacier cirque, spanning an elevation difference of 900 m. We test the validity of on-glacier linear lapse rates, and a model that calculates Ta based on the influence of katabatic winds and other heat sources along the glacier flow line. During the coldest hours of each summer (10% of time), average lapse rates across the entire glacier range from −4.7 to −6.7°C km−1, with a strong relationship between Ta and elevation (R2 > 0.7). During warm conditions, Ta shows more complex, non-linear patterns that are better explained by the flow line-dependent model, reducing errors by up to 0.5°C compared with linear lapse rates, although more uncertainty might be associated with these observations due to occasionally poor sensor ventilation. We conclude that Ta spatial distribution can vary significantly from year to year, and from one glacier section to another. Importantly, extrapolations using linear lapse rates from the ablation zone might lead to large underestimations of Ta on the upper glacier areas.


2008 ◽  
Vol 47 (1) ◽  
pp. 249-261 ◽  
Author(s):  
Troy R. Blandford ◽  
Karen S. Humes ◽  
Brian J. Harshburger ◽  
Brandon C. Moore ◽  
Von P. Walden ◽  
...  

Abstract To accurately estimate near-surface (2 m) air temperatures in a mountainous region for hydrologic prediction models and other investigations of environmental processes, the authors evaluated daily and seasonal variations (with the consideration of different weather types) of surface air temperature lapse rates at a spatial scale of 10 000 km2 in south-central Idaho. Near-surface air temperature data (Tmax, Tmin, and Tavg) from 14 meteorological stations were used to compute daily lapse rates from January 1989 to December 2004 for a medium-elevation study area in south-central Idaho. Daily lapse rates were grouped by month, synoptic weather type, and a combination of both (seasonal–synoptic). Daily air temperature lapse rates show high variability at both daily and seasonal time scales. Daily Tmax lapse rates show a distinct seasonal trend, with steeper lapse rates (greater decrease in temperature with height) occurring in summer and shallower rates (lesser decrease in temperature with height) occurring in winter. Daily Tmin and Tavg lapse rates are more variable and tend to be steepest in spring and shallowest in midsummer. Different synoptic weather types also influence lapse rates, although differences are tenuous. In general, warmer air masses tend to be associated with steeper lapse rates for maximum temperature, and drier air masses have shallower lapse rates for minimum temperature. The largest diurnal range is produced by dry tropical conditions (clear skies, high solar input). Cross-validation results indicate that the commonly used environmental lapse rate [typically assumed to be −0.65°C (100 m)−1] is solely applicable to maximum temperature and often grossly overestimates Tmin and Tavg lapse rates. Regional lapse rates perform better than the environmental lapse rate for Tmin and Tavg, although for some months rates can be predicted more accurately by using monthly lapse rates. Lapse rates computed for different months, synoptic types, and seasonal–synoptic categories all perform similarly. Therefore, the use of monthly lapse rates is recommended as a practical combination of effective performance and ease of implementation.


2013 ◽  
Vol 118 (14) ◽  
pp. 7505-7515 ◽  
Author(s):  
Xiuping Li ◽  
Lei Wang ◽  
Deliang Chen ◽  
Kun Yang ◽  
Baolin Xue ◽  
...  

2021 ◽  
Author(s):  
Çağrı Hasan Karaman ◽  
Zuhal Akyurek

<p>Near surface air temperature is a key variable used in wide range of applications showing environmental conditions across the earth. Standard meteorological observations generally provide the best estimation with high accuracy over time for a small area of influence. However, considerable uncertainty arises when point measurements are extrapolated or interpolated over much larger areas. Satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. Thus, spatial patterns of air temperature can be derived from satellite remote sensing.</p><p>In this study, we evaluate the performance of several satellite-based products of near surface air temperature to determine the best product in estimating daily and monthly air temperatures. Era5 Land, SMAP Level 4, AgERA5, MERRA2 products are used with 1120 ground-based gauge stations for the period 2015-2019 over complex terrain and different climate classes according to Köppen-Geiger climate classification in Turkey. Moreover, several traditional and more sophisticated machine learning downscaling algorithms are applied to increase products’ spatial resolution. The agreement between ground observations and the different products and the downscaled temperature product is investigated using a set of commonly used statistical estimators of mean absolute error (MAE), correlation coefficient (CC), root-mean-square error (RMSE) and bias.</p><p>Performance analysis of satellite-based air temperature products with ground-based observations on monthly time series has shown that ERA5 Land and SMAP L4 products have similar capabilities. However, analysis on daily time series depicted that ERA5 Land is superior to SMAP L4 product. Results indicate that bicubic interpolation performs best on downscaling Era5 Land product daily time series. However, Random Forest algorithm is superior on monthly time series.</p>


2018 ◽  
Vol 38 (8) ◽  
pp. 3233-3249 ◽  
Author(s):  
F. Navarro-Serrano ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
E. Alonso-González ◽  
M. Tomás-Burguera ◽  
...  

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