scholarly journals Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

2018 ◽  
Author(s):  
Rose Petersky ◽  
Adrian Harpold
Keyword(s):  
Soil Moisture ◽  
Great Basin ◽  
Blowing Snow ◽  
Time Period ◽  
Moderate Resolution ◽  
Snow Distribution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Strong Control

Abstract. Ephemeral snowpacks, or those that routinely experience accumulation and ablation at the same time and persist for <60 days, are challenging to observe and model. Using 328 site years from the Great Basin, we show that ephemeral snowmelt delivers water earlier than seasonal snowmelt. For example, we found that day of peak soil moisture preceded day of last snowmelt in the Great Basin by 79 days for shallow soil moisture in ephemeral snowmelt compared to 5 days for seasonal snowmelt. To understand Great Basin snow distribution, we used moderate resolution imaging spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) data from water years 2005–2014 to map snow extent. During this time period snowpack was highly variable. The maximum seasonal snow cover was 64 % in 2010 and the minimum was 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal. Snowpacks were more likely to be ephemeral on south facing slopes than north facing slopes at elevations above 2500 m. Additionally, we used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain-snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, we put forward several challenges and recommendations to bolster physics based modeling of ephemeral snow such as better metrics for snow ephemerality and more ground-based observations.

scholarly journals Now you see it, now you don't: a case study of ephemeral snowpacks and soil moisture response in the Great Basin, USA

2018 ◽  
Vol 22 (9) ◽  
pp. 4891-4906 ◽  
Author(s):  
Rose Petersky ◽  
Adrian Harpold
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Great Basin ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Deep Soil ◽  
Time Period ◽  
Snow Distribution ◽  
Snow Cover Duration ◽  
Strong Control

Abstract. Ephemeral snowpacks, or those that persist for < 60 continuous days, are challenging to observe and model because snow accumulation and ablation occur during the same season. This has left ephemeral snow understudied, despite its widespread extent. Using 328 site years from the Great Basin, we show that ephemeral snowmelt causes a 70-days-earlier soil moisture response than seasonal snowmelt. In addition, deep soil moisture response was more variable in areas with seasonal snowmelt. To understand Great Basin snow distribution, we used MODIS and Snow Data Assimilation System (SNODAS) data to map snow extent. Estimates of maximum continuous snow cover duration from SNODAS consistently overestimated MODIS observations by >25 days in the lowest (<1500 m) and highest (>2500 m) elevations. During this time period snowpack was highly variable. The maximum seasonal snow cover during water years 2005–2014 was 64 % in 2010 and at a minimum of 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal except those on south-facing slopes. Additionally, we used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain–snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, physics-based modeling is needed that can account for the complex energetics of shallow snowpacks in complex terrain. These modeling efforts will need to be supported by field observations of mass and energy and linked to finer remote sensing snow products in order to track ephemeral snow dynamics.

scholarly journals Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

2014 ◽  
Vol 7 (2) ◽  
pp. 1671-1707
Author(s):  
J. Kala ◽  
J. P. Evans ◽  
A. J. Pitman ◽  
C. B. Schaaf ◽  
M. Decker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Surface Model ◽  
Parameter Uncertainties ◽  
Australian Continent ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.

scholarly journals Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Miro Govedarica ◽  
Dušan Jovanović ◽  
Filip Sabo ◽  
Mirko Borisov ◽  
Milan Vrtunski ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Corn Yield ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractThe aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (

scholarly journals Adaption of the MODIS aerosol retrieval algorithm using airborne spectral surface reflectance measurements over urban areas: a case study

2015 ◽  
Vol 8 (12) ◽  
pp. 5237-5249 ◽  
Author(s):  
E. Jäkel ◽  
B. Mey ◽  
R. Levy ◽  
X. Gu ◽  
T. Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Airborne Measurements ◽  
Modis Aod ◽  
Urban Sample ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. MODIS (MOderate-resolution Imaging Spectroradiometer) retrievals of aerosol optical depth (AOD) are biased over urban areas, primarily because the reflectance characteristics of urban surfaces are different than that assumed by the retrieval algorithm. Specifically, the operational "dark-target" retrieval is tuned towards vegetated (dark) surfaces and assumes a spectral relationship to estimate the surface reflectance in blue and red wavelengths. From airborne measurements of surface reflectance over the city of Zhongshan, China, were collected that could replace the assumptions within the MODIS retrieval algorithm. The subsequent impact was tested upon two versions of the operational algorithm, Collections 5 and 6 (C5 and C6). AOD retrieval results of the operational and modified algorithms were compared for a specific case study over Zhongshan to show minor differences between them all. However, the Zhongshan-based spectral surface relationship was applied to a much larger urban sample, specifically to the MODIS data taken over Beijing between 2010 and 2014. These results were compared directly to ground-based AERONET (AErosol RObotic NETwork) measurements of AOD. A significant reduction of the differences between the AOD retrieved by the modified algorithms and AERONET was found, whereby the mean difference decreased from 0.27±0.14 for the operational C5 and 0.19±0.12 for the operational C6 to 0.10±0.15 and -0.02±0.17 by using the modified C5 and C6 retrievals. Since the modified algorithms assume a higher contribution by the surface to the total measured reflectance from MODIS, consequently the overestimation of AOD by the operational methods is reduced. Furthermore, the sensitivity of the MODIS AOD retrieval with respect to different surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectance data were used as input for the retrieval methods. It was shown that the operational MODIS AOD retrieval over land reproduces the AOD reference input of 0.85 for dark surface types (retrieved AOD = 0.87 (C5)). An overestimation of AOD = 0.99 is found for urban surfaces, whereas the modified C5 algorithm shows a good performance with a retrieved value of AOD = 0.86.

scholarly journals Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

2012 ◽  
Vol 51 (10) ◽  
pp. 1811-1822 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Richard Dworak ◽  
Jason Brunner ◽  
Wayne Feltz
Keyword(s):  
Weather Prediction ◽  
Convective Cloud ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Detection Methods ◽  
Spatial Gradients ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractTwo satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-μm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD > 0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

scholarly journals Estimation of spatio-temporal distribution of precipitable water using MODIS and AVHRR data: a case study for Cyprus

2011 ◽  
Vol 30 ◽  
pp. 23-29 ◽  
Author(s):  
D. Hadjimitsis ◽  
Z. Mitraka ◽  
I. Gazani ◽  
A. Retalis ◽  
N. Chrysoulakis ◽  
...  
Keyword(s):  
Near Infrared ◽  
Temporal Distribution ◽  
Grid Cell ◽  
Precipitable Water ◽  
Avhrr Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. In this paper, the atmospheric precipitable water (PW) over the area of Cyprus was estimated by means of Advanced Very High Resolution Radiometer (AVHRR) thermal channels brightness temperature difference (ΔT). The AVHRR derived ΔT was calculated in a grid of 5 × 5 km cells; the corresponding PW value in each grid cell was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 product (near-infrared algorithm). Once the PW – ΔT relationship coefficients corresponding to the area of Cyprus were calculated, the relationship was applied to AVHRR data for one month period. Radiosonde derived PW values, as well as MODIS independent PW values were used to validate the estimations and a good agreement was noted.

scholarly journals Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia

2016 ◽  
Vol 16 (3) ◽  
pp. 1255-1269 ◽  
Author(s):  
Q. Xiao ◽  
H. Zhang ◽  
M. Choi ◽  
S. Li ◽  
S. Kondragunta ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Infrared Imaging ◽  
Environmental Data ◽  
Urban Centers ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.

scholarly journals Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Hyunglok Kim ◽  
Muhammad Zohaib ◽  
Eunsang Cho ◽  
Yann H. Kerr ◽  
Minha Choi
Keyword(s):  
Soil Moisture ◽  
Short Term ◽  
Good Correspondence ◽  
Ocean Salinity ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Desert Areas ◽  
Land Data Assimilation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Data Assimilation System

For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMOS-based MA (SMOS-MA) dataset showed good correspondence with observed AOD (R-value: 0.56) compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively lowR-value (0.35) with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a highR-value (0.65). The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis.

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