scholarly journals A baseline evaluation of atmospheric and river discharge conditions in the Hudson Bay Complex during 2016–2018

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Andrew Tefs ◽  
Shabnam Jafarikhasragh ◽  
Clark Pennelly ◽  
Paul G. Myers ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Land Surface ◽  
River Discharge ◽  
Time Frame ◽  
Hudson Bay ◽  
Sea Level Pressure ◽  
Weather Forecasts ◽  
Baseline Evaluation ◽  
Land Surface Temperatures ◽  
High Precipitation

In this article, we examine atmospheric and river discharge conditions within the Hudson Bay Complex for the BaySys 2016–2018 field program time frame. Investigated in particular is a subset of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis - Interim (ERA-Interim) atmospheric forcing variables, namely 2-m surface temperature, 10-m surface winds, precipitation, and sea-level pressure, in addition to river discharge. Results from this assessment show that 2016 was characterized by unusually warm conditions (terrestrial and marine) throughout the annual cycle; 2017 by strong cyclone activity in March and high precipitation in January, October, and November; and 2018 by cold and windy conditions throughout the annual cycle. Evaluation of terrestrial conditions showed higher than normal land surface temperatures (the Hudson Bay physical watershed) for all of the 2016–2018 period (excluding a colder than normal spell August–November 2018), particularly in January (2016 and 2017), higher than normal precipitation in October (2016 and 2017), and higher than normal terrestrial discharge to the Hudson Bay Complex in March (2016 and 2017), with drier than average June through October (2016–2018).

scholarly journals An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

2021 ◽  
Vol 13 (17) ◽  
pp. 3522
Author(s):  
Thomas P. F. Dowling ◽  
Peilin Song ◽  
Mark C. De Jong ◽  
Lutz Merbold ◽  
Martin J. Wooster ◽  
...  
Keyword(s):  
Land Surface ◽  
Cloud Cover ◽  
Time Frame ◽  
Passive Microwave ◽  
Gap Filling ◽  
Coarse Spatial Resolution ◽  
Land Surface Temperatures ◽  
Filling Method ◽  
Microwave Techniques ◽  
Mean Square Errors

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

scholarly journals A baseline evaluation of oceanographic and sea ice conditions in the Hudson Bay Complex during 2016–2018

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Shabnam Jafarikhasragh ◽  
A. Tefs ◽  
Paul G. Myers ◽  
K. Sydor ◽  
...  
Keyword(s):  
Sea Ice ◽  
Meridional Circulation ◽  
Extreme Event ◽  
Ice Cover ◽  
Time Frame ◽  
River Regulation ◽  
Hudson Bay ◽  
Baseline Evaluation ◽  
Ice Conditions ◽  
Strong Winds

In this paper, we examine sea surface temperatures (SSTs) and sea ice conditions in the Hudson Bay Complex as a baseline evaluation for the BaySys 2016–2018 field program time frame. Investigated in particular are spatiotemporal patterns in SST and sea ice state and dynamics, with rankings of the latter to highlight extreme conditions relative to the examined 1981–2010 climatology. Results from this study show that SSTs in northwestern Hudson Bay from May to July, 2016–2018, are high relative to the climatology for SST (1982–2010). SSTs are also warmer in 2016 and 2017 than in 2018 relative to their climatology. Similarly, unusually low sea ice cover existed from August to December of 2016 and July to September of 2017, while unusually high sea ice cover existed in January, February, and October of 2018. The ice-free season was approximately 20 days longer in 2016 than in 2018. Unusually high ice-drift speeds occurred in April of 2016 and 2017 and in May of 2018, coinciding with strong winds in 2016 and 2018 and following strong winds in March 2017. Strong meridional circulation was observed in spring of 2016 and winter of 2017, while weak meridional circulation existed in 2018. In a case study of an extreme event, a blizzard from 7 to 9 March 2017, evaluated using Lagrangian dispersion statistics, is shown to have suppressed sea ice deformation off the coast of Churchill. These results are relevant to describing and planning for possible future pathways and scenarios under continued climate change and river regulation.

scholarly journals Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

2016 ◽  
Vol 9 (1) ◽  
pp. 23 ◽  
Author(s):  
Panagiotis Sismanidis ◽  
Iphigenia Keramitsoglou ◽  
Benjamin Bechtel ◽  
Chris Kiranoudis
Keyword(s):  
Annual Cycle ◽  
Land Surface ◽  
Surface Temperatures ◽  
Land Surface Temperatures

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures
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