Questions related to Snow
It is said that due to high surface reflectance(bright surface) of snow covered region, estimation of AOD using spectral channels of sensors onboard satellites like MODIS, SUOMI-NPP and Sentinel-3, is limited. I am studying snow melt in Himalayan region where Aerosol Optical depth is crucial for correct estimation of Radiation processes. Are there any methods (or) alternative satellite sensors (or) indirect methods to capture the spatio-temporal variability of AOD over Himalayan snow covered regions?
I am working on loads such as snow load, and I was wondering how it is achievable to assign a uniform horizontal projected load to a spherical shell (i.e. not applied to the reference surface)?
I am working on a study of sample size 100 collected through snow ball technique using referral method. Can you please guide that in order to achieve my objectives, should I apply parametric techniques ( as sample size is 100) or should apply non- parametric techniques ( as samples are not selected randomly) ??
Please pour your valuable suggestions
Hello Dear Users!
I have a problem with low value of surface runoff. In my output.std is:
PRECIP = 620.9 MM
SNOW FALL = 52.53 MM
SNOW MELT = 52.25 MM
SUBLIMATION = 0.28 MM
SURFACE RUNOFF Q = 5.90 MM
LATERAL SOIL Q = 11.97 MM
TILE Q = 0.00 MM
GROUNDWATER (SHAL AQ) Q = 138.97 MM
GROUNDWATER (DEEP AQ) Q = 52.86 MM
REVAP (SHAL AQ => SOIL/PLANTS) = 55.96 MM
DEEP AQ RECHARGE = 53.14 MM
TOTAL AQ RECHARGE = 192.12 MM
TOTAL WATER YLD = 210.15 MM
PERCOLATION OUT OF SOIL = 193.78 MM
ET = 415.9 MM
PET = 621.6MM
My calibration NS is 0.73. How parameters can increase the value for surface runoff?
I am working on building a snow melt runoff model using satellite based Snowcover, Snow albedo, Snow temperature, AOD, etc.,. Also using global forecasted air temperature datasets. I am looking for a test basin for my model where Snow water equivalent, Snow depth, Snow temperature, Snowmelt runoff and air temperature records for multiple years are available over multiple snow melt seasons. Any suggestions?
Hello, everyone. I don't quite understand some physical mechanisms when I am doing research on snow depth of lidar. I hope I can get help.
When I read the previous literature, I learned that the longer the wavelength of electromagnetic wave, the stronger the ability to "bypass" snow and the higher the penetration depth. But why is 532 more penetrating than 1064nm in lidar?(artile:Lidar measurement of snow depth: a review."doi:10.3189/2013JoG12J154)
- I use RIGEL's miniVUX-1UAV sensor, which has a wavelength of 905nm. For this wavelength, the drone receives the reflected signal from the top 10cm of snow(DOI:10.3189/2013JoG12J154). Therefore, I wonder if it is possible to use Lidar classification method as mentioned in the paper (doi:https://doi.org/10.5194/tc-2020-37)to classify the point as ground and non-ground point , i.e., to get the snow depth using the data at once? Is the physical mechanism reasonable for snow (Snow reflectivity is high and penetration is low)?
I am working on a precipitation analysis and have data for two rain gauges. I also have air temperature. What is the best way to determine precipitation type? What is a good temperature to assume anything above is rain and anything below is snow? I assume there is also an uncertainty range (-1C to 4C?)?
Any help would be much appreciated!
Any downscaled reanalysis/satellite based high resolution (less than 10km) temperature data available for whole HKH region which can be used for snow research for time domain 2000-2015?
I am currently working on my masters thesis to maximize water system performance in the Chilean Laja River basin under climate change. I need historical observed data for streamflow of the rivers Tucapel and Puente Perales. Similarly, I need snow accumulation data for the basin as well.
Could someone please kindly suggest, where/how I could find these data? Has anyone downloaded these data from internet. I would be grateful for any suggestions.
Thank you in advance!
Hi. I want to research about geothermal and find relationship between geothermal and snow cover ( snow melting) and vegetation stress. i am looking for an idea about how can i do this? and the important qustion is can i do this with optical sensors?
I'm looking to use this model to get the Snow Water Equivalence (SWE) from my snow height data but I'm having a hard time understanding how to actually start using this model/where to put the inputs etc. If anyone has some experience using this model it would be greatly appreciated!
Alternatively if you have another suggestion on how to obtain SWE from snow height (no snow density available, however climate data is) it would be greatly appreciated!
Thanks in advanced!
I have already calculated the original NDSI index, and I would like to use PCA analysis for more accurate results. I read in a study that I need the brightest and the darkest component for the index calculation (NDPCSI). I also read that this corresponds to the PCA component 1 and component 2. That is also mean Band 1 and Band 2? (if not, which bands should I use) Because I did the PCA analysis with Landsat 8 Bands (1-8), but I'm not sure what I supposed to use. I am a little bit confused about it.
Somebody could help me in this case, please?
I would be very glad if you tell me.
I am looking to get snowfall data or temporal snow cover data from non-modeled data platforms. If anyone has some information then please share it.
Is there a literature review or a comprehensive study on the unsaturated of various soils under the influence of snow that is gradually accumulated on the ground over winter?
Thank you in advance.
I'm troubleshooting Fv/Fm measurements on dark-adapted snow algae samples in the field. We have a field season coming up in the North Cascade Mountains here in Washington State. I have an Opti-Sciences OS1p flourometer. If you have insight into the process, I would love to hear about your successes and struggles.
The unknown factors I forsee coming up are:
- Does the probe need to be submerged in the sample?
- What is the ideal "slushiness" of the sample? i.e. how much water vs. snow?
- How long to dark-adapt samples?
- How much algal tissue is necessary for accurate readings?
Thanks for your thoughts.
I am modeling the impact of snow events on traffic flow to extract the relationship between snow events and traffic flow. Known the pattern of the relationship between these 2 variables will be used in the prediction model.
Giving an assumption of snow value, I want to predict the traffic flow value.
I have historical spatiotemporal weather & traffic data, and I was thinking to use trajectories modeling approach to analyze the relationship between these 2 variables in my data.
Then using the trajectory model outcome to train the prediction model in order to predict the traffic flow value.
Not sure if my methodology would achieve my goal or if there's any research work similar to this approach.
I would greatly appreciate it if you share your thoughts.
I have snow parameters calculated using MODIS snow products and I'm trying to validation with ERA5-Land monthly averaged - ECMWF climate reanalysis images using Pearson correlation. MODIS snow products have spatial resolution of 500 m while ERA-5 has 0.1 degree (11.1 km) of spatial resolution, thus terrible for me to correlation, even resizing back to 500m doesn't make sense. Could one of you be so kind for any advices, please?
There's any software able to efficiently simulate snowmelt runoff paths on a specific surface (also looking at the geomorphology, e.g. considering available DEMs), starting from local snowmelt runoff measures?
>> More details: I have punctual values of snowmelt runoff (m3/s), crossing the end section of a watershed, but I would like to find out a solution so as to spatially simulate snowmelt runoff flows on the entire watershed, looking at the values obtained to its end section.
in the years I've used R extensively (mostly the package ggplot2) in order to produce high resolution plots of my data. Since I'm about to start a new project that involves analyzing snow samples across Antarctica, I want to plot the different locations on the continent... problem is, I mostly plot with R, and I'd like to keep using it, but when I search for how to plot coordinates in R, most people focuses on the rest of the world and discard the Antarctic continent. Does anyone know any way to quickly plot coordinates in R? Or direct me to some sources I can study in order to solve this problem?
The simulation of the snowmelt runoff using the SRM model seems to be characterized by some issues regarding the inclusion of wind data in input setting. Since wind data have a primary role in snow accumulation, how can I efficiently include this factor in order to preserve the accuracy of SRM simulations?
I'm doing my time-series research using MODIS snow products' snow albedo (version 6) band to calculate daily snow water equivalent. However, my study area is heavily cloud-contaminated. Therefore, the majority of the pixels have been masked out due to the MODIS algorithm. The pixels are not enough to analyze for seasonal behavior of snow water equivalent. I found MCD43A1.006 MODIS BRDF-Albedo Model Parameters Daily 500m. But it is wavelength-specified albedos. Will it be possible to apply the bands corresponding to the snow reflectance curve? Thank you in advance.
I am working on a risk assessment and management, integrating physical and social sciences, for a high altitude mountainous region. The area is prone to multiple natural hazards, particularly snow storm and avalanche. There are literatures on social vulnerability to earthquake, flood, drought, cyclone and hurricane; but, could not find any on avalanche. So, I would like to know if there are similar research relating to avalanche and what indicators are good for understanding social vulnerability to avalanche.
I need the albedo value on the ground to be able to calculate the solar radiation for a location. I know that the albedo value is not the same for oceans, snow, ... etc.
I would like to know is the albedo value for a site varies every hour or remains constant?
thank you in advance
I am looking for any information about the relationships between the snowfall or snow cover with an altitude over the Tibetan plateau. I know that in general, the precipitation is lower at a higher altitude, And the temperature trend is opposite but I am mainly interested in how long the snow cover lasts at different altitudes. Probably saying that the temperatures are lower and the cause of that snow lasts longer is far too simple.
Do you have any information about that or know published papers?
Is there a high spatial and temporal resolution snow depth dataset available?
I would like to inquire if there is a recommended snow depth dataset covering China with high spatial and temporal resolution in long term. The resolution should preferably be higher than daily 25km×25km. Thanks!
Hi everyone, is there any paper or model which relates the factors which control snowfall rates? The idea is to quantify the effect of climatic/geographical conditions to justify the existence of snow over highlands.
The logistic functions relate the occurrence according to the temperature, however, they do not take into account the effect of altitude or vapor pressure directly. Please suggest to me any research that integrates these issues.
Thanks a lot for your help
Welcome, all Suggestions for mitigation of the impact of Spatio-temporal change on Snow, Vegetation, and Timberline in the Indian Himalaya.
What are the best loggers for obtaining changes in snow cover temperature at different depths with changes in air temperature? What are the long-term data loggers without recharging? What loggers come with a great software product?
I am doing my project work in snowmelt runoff modelling. I have collected a Matlab code which I found very difficult to understand due to its complexity.
Any help on compilation of SWAT source code into SWAT.exe would be much appreciated
We obtained the global results using remote sensing reanalysis dataset. But the snow-coverd areas should be removed (like the blank area in the picture below, sources: Purdy et al., 2016). Does anyone know where we could obtain the snow data as mask? Thanks a lot!
We know that heating happens due to mid-infrared region of solar irradiation.
Ice melts due to absorption of which specific range of wavelengths of electromagnetic spectrum?
Can we relate this to the vibration states of hydrogen bonds inside ice crystals and due to water molecules?
And is all the energy absorbed in UV, Infrared and Microwave region used for heating the ice mass or could it be used for breaking OH bond and just lead to ionisation?
Could you pleas tell me if I used snow ball sample (nonrandom sample) to achieve the following objectives by using SMART-PLS.
1- to determine the relationship between I.V1 and D.V. , I have used path coefficient (path analysis) only.
2- to determine the effect of I.V1 and I.V2 on D.V, I used path coefficients , R2 and effect size (f2) only (without applying construct validity, decrement validity).
is it correct steps?
Thank you so much.
Do you any publicly accessible ground observations which have both solid and liquid precipitation rate? Preferably for many sites in different regions.
Assuming that ground measured data is the most reliable for the snowfall rate.
I am using HBV model with daily temperature and precipitation as its input. Accurate and reliable winter precipitation is hard to measure in prairie and other cold regions, but weather station data is still only sources to run hydrological models. So I have snow survey data as supplements, where snow survey is usually conducted right before snow melt beginning.
How could I incorporate snow survey data into daily precipitation record so that I can use HBV model to simulate spring snow melt runoff? Or you have other options, such as using re-analyzed data, to go forward?
Unlike Microwave data, optical data is not sensitive to snow depth and internal snow pack properties. But Is there any other way with which it is possible to estimate snow volume using optical remote sensing data. I could not find any relevant publications on this topic. Kindly share links of articles, or any ideas in this connection. I appreciate the time and effort taken for answering this query.
I faced a problem while using Landsat 8 OLI surface reflectance product. The images of the same scene captured on different dates show very different contrast in colour. Any particular Radiometric/Atmospheric corrections required?
Sun angle, cloud/snow cover are not there, I am sure for that. Because it is a plain topograhy and the selected images are with less than 10% scene cloud cover.
Please provide technical answers only matching with the question.
Snow fences are using in cold regions of different countries (US, Canada etc.) to prevent the road casualties along the roads. I would like to study for the feasibility of using snow fence in Nepal. Can you please let me know if you have any idea about it or if you have done any research about it in the context of Nepal?
- Keshav Basnet
my questions are about HRLDAS. I am runing HRLDAS with GLDAS-based forcing data.
For running offline LSM such HRLDAS, we know we need atmospheric forcing and initial data.
In case of HRLDAS, we need forcing data such as
Rainf/ Snowf/ Wind/ Tair/ Qair/ Psurf/ SWdown/ LWdown/ SWdown24/ Precip/ U/ V/
and initial data such as
SWE/ Canopint/ AvgSurfT/ SoilM/ Tsoil
from Reanalysis data or other land assimilation data. (my case, GLDAS)
My question in this step is, why it need combine_precipitation and hourly interpolation of solar radiation?
I mean, from forcing data, we extracted snow and rain separately, but next step is to combine them as a precipitation. Is there any reason for this?
And why should we need to do hourly interpolation of solar radiation via zenith angle using 3hrly solar radiation? why only this parameter?
Thanks for time and for reading my question!
Thanks in advance.
My email if necessary : firstname.lastname@example.org
I’m exploring monitoring of snow cover on the river, I used the NDSI NDWI index, but they show me that snow is water and water is snow, how can I distinguish them?
A lot of people suggested to use Water and change the CN value afterwards but I already have Water and Barren land as other classes.
For several weeks, the ice surrounding Vernadsky's research base in Antarctica has looked like a bloodbath.
In recent weeks, the ice surrounding the Ukrainian research base in Vernadsky, Antarctica, has been covered with what researchers call "raspberry snow". A sight that may sound scary but has a simple scientific explanation, says British media The Sun.
What looks like a bloodbath is actually linked to the presence of a red-pigmented alga, called Chlamydomonas nivalis. The latter develops in icy water and generally remains dormant under snow and ice until summer melts some of the snow. Once in the sun, its red pigment helps the algae absorb heat and thrive. As a result, the presence of this organism has the effect of causing faster melting of snow and ice since the more it absorbs heat, the faster the surrounding ice melts.
A phenomenon that worries some scientists. On Facebook, the Ministry of Education and Science of Ukraine believes that these algae "contribute to global warming".
A vicious circle
"Because of its crimson red color, snow reflects less sunlight and melts faster. As a result, more and more algae are produced", the ministry said. The more algae, the more the ice melts and the less ice there is, the more the algae spread. A vicious circle exacerbated by high temperatures.
This phenomenon is not unusual. However, if global temperatures continue to rise, these strange events will eventually become more frequent and help melt snow and ice faster.
From physics perspective the ice or snow are good in reflecting the solar radiation and the land is a good absorber of solar radiation. Let consider scientist who climb the top on mountain covered with glacier for ice core drilling, hope they may expose the land covered by ice to solar radiation and this may accelerate the melting of ice over the mountain. I would like to know is this could explain the melting of glacier for example over mountain Kilimanjaro. Or what are the impact of ice core drilling over tropical mountains?
I am currently doing literature review on snow depth retrieval methods using passive remote sensing. While the algorithm in Chang (1987) says it uses brightness temperature difference at 18 and 37 GHz frequencies (Horizontal)(https://nsidc.org/sites/nsidc.org/files/technical-references/amsr-atbd-supp12-snow.pdf) in few other papers it is mentioned as vertical polarisation(
I would be grateful for all the responses and time taken to answer this query.
I am looking for the techniques used in SAR data processing to differentiate the pixels of snow, ice, debris and waterbody.
I am not sure which projection system would be best fit to study snow cover of Himalayan mountains through MODIS snow cover data.
How much differ the result of WGS84 and EPSG projection system?
I have two raster one is elevation class map from DEM reclassification and another is snow cover fraction map. I need to calculate elevation class wise Snow cover fraction(%). I do not want to convert the input raster format to vector format because in the later stage I need the output file in raster format.
Topographic effect, data scarcity and complex atmospheric system makes the precipitation of Himalaya mountains very dynamic. Down scaling of precipitation in such area without considering orographic, laps rate, rain shadow, snow, seasonal and annual shift of precipitation phenomena is not much promising.
So, what are best possible ways to down scale the remotely sensed, reanalysis and model output precipitation product over Himalaya mountains by considering the above factors?
Thank you in advance
My question is regarding suggested methodologies for snow sampling in, for example, mountains or peeks. Some ice sampling techniques for these environments would also be appreciated. Must consider these samples are going to be processed to identify microplastics in the snowy mountain ecosystems.
Thanks in advance,
I would like to use Snowmelt Runoff Model (SRM) for my research, I need to interpolate MODIS 8-Day snow cover data in to daily. although based on the basin characteristics I have extracted 9 elevation zones, therefore I need to extrapolate temperature and precipitation data considering 9 zones.
Please help me by your suggestions.
Thanks in advanced.
A place near Quetta about 40km west to quetta called jangal pir alizai has salt underground water. And in winter ,this region earth surface change their surface colour as white as like snow. But it is due to salinity at the region. So what is the reason of this region plate?
I am new to survival analysis and need advice on how to correctly analyze some data (with R).
Along an elevation gradient, we have 5 different sites located at 5 different altitudes (600, 1000, 1400, 1600, 2000 m asl). At each site we have 30 different species (belonging to the Brassicaceae family) coming from 3 different altitudes (high-, mid- and low- elevation species). Each species is represented by 20 individuals (10 maternal lines (5 for each population) x 2 replicates).
The plants were germinated in controlled conditions (about 3 weeks before being moved in field) and moved at the different sites when the predicted environmental temperature had similar values (so at 2000m the plants were placed in August, at 600m in October).
For each individual, survival was checked each week (binary, 0/1 where 1 died). In addition, for another study we also recorded flowering (0/1) and fruiting (0/1). Measurements stopped when the sites were inaccessible due to the snow (sites covered and/or road blocked), and started again when sites were are accessible again (i.e. the measurement time is different for each site depending on the snow cover). At the end of the experiment, numerous individuals are still alive.
Finally, at each site we have hourly temperature measurements.
What we want to test
i) if the interaction site_elevation * species_origin influences mortality (e.g., lowland species have a higher mortality ad higher sites) and ii) if mortality is affected by a specific temperature (and if different temperatures affect different elevation class).
The data file structure Site (factor, 5 levels): "600", "1000", "1400", "1600", "2000"; Species (factor, 30 levels): 30 species; phylogeny is available (.nexus); Elev_class (factor, 3 levels): "high", "mid", "low", is the altitudinal class of the 30 species; Elev_m (numeric): median altitude of the 30 species; ID (factor, 3000 levels): unique id for each individual (e.g., 600_1_1_1); Pop (factor): unique id for each population; Fam (factor): a factor (1, 2, 3, ...) indicates the maternal line for each population, it is not unique (e.g., both in pop1 and in pop2 we can have fam 1); Measurement_Date (date): date on which the binary response was measured, in standard format (e.g., 2019-08-31); Week_field (integer): this is a temporal measure assigns 1 to the first week where the plants have been moved to the respective sites (1, 2, 3 ...); Temp (numeric): a variable that represents the temperature (e.g., median) of the week preceding the measurement (e.g., at survival of week 3 is associated the temperature value between week 2 and week 3); Survival: binary variable 0/1 which describes whether the plant is alive or dead on the respective date / week
i) I need to correct for phylogeny. This prevents me from using models like Kaplan-Meier. I am currently trying with MCMCglmm. Is there another possibility?
ii) From what I understand I have to model the response (survival) using a longitudinal mixed effect model (since each individual is measured weekly, pseudo-replicate). For example, to check if a temperature affects survival, the model is: survival ~ Elev_class * Temp * Week_field random = phylogeny + Site + us (Week_field):ID family = "threshold" However, not all individuals suffer the event (death) during the time of the experiment and from what I understand they are right censored data. Does this also apply to a logistic model or only for a time-to-event model? If yes how should it be corrected?
iii) In lowlands class I have some species that are annual, so mortality after fruiting is not necessarily linked to environmental factors. I am currently trying to make two models, one including only the data up to fruiting (but keeping all the species), and one with the totality of the measures but excluding the annuals. Is it a good procedure? Is there a better way or a way to have a single model that considers this difference?
iv) The temperature varies with time ( in autumn the temperature decreases with the wintering, while in spring this increases with the time). Is this correlation a problem in the model having both time (longitudinal mixed model) and temperature?
Any other suggestions, ideas and / or references are welcome. Thanks in advance!
I have multiple miniDOT DO loggers deployed in small mountain streams. Getting data on DO through the winter would be valuable, but at the same time, I don't want to damage our sensors. The PME site suggests that the loggers function in the cold as long as they are in liquid water, but as we weigh whether to keep or remove the sensors this winter, I would be interested to hear about other people's experiences/perspectives on logger and battery performance over long time periods in cold water . Thanks!
Different moisture source, amount, pattern. landscape and slope are contributing in snow cover of both eastern and western sides of Tibetan plateau. Western side has more glaciated area and relatively less know hydrological cycle as compare to eastern side.
I am looking for reasons of difference beyond above mentioned features in perspective of remote sensing particularly MODIS snow cover.
Earth is in more hot water than ever before: Sea levels are rising at an ever-faster rate as ice and snow shrink, and oceans are getting more acidic and losing oxygen, the Intergovernmental Panel on Climate Change said in a report issued as world leaders met at the United Nations. https://www.ipcc.ch/srocc/download-report/
I am interested in using Landsat 5-8 images to map snow and ice cover. I am trying to construct a time series showing how late into the year snow and ice cover lasts. I noticed that for Landsat ARD tiles obtained from USGS Earth Explorer there is a Pixel Quality Assessment band that accompanies surface reflectance products and that this PQA raster includes bit designations for pixels where snow or ice are present (bits 80 and 144 for Landsat 4/5). After reading more I have gathered that this PQA product is generated using the Fmask algorithm which was developed primarily for generating cloud masks. However, I decided to employ these products to see how they perform when generating fractional snow cover rasters.
I noticed that for some images very late into the year (May and June) the Fmask algorithm did classify many pixels as snow or ice, although after generating RGB composites and using the thermal infrared band to look at temperature, I determined that there was no snow or ice cover present in the image although it did look like some clouds were present. After reading more of the literature I found out that the Fmask algorithm has a tendency to sometimes classify cloud pixels as snow or ice, but I could not find an explanation as to why this happens. Is there a particular cloud type that the algorithm classifies as snow or ice, or is it unpredictable? Is there a better algorithm that is designed specifically for generating maps of fractional snow cover?
Thanks for you help,
I have sum of crossponding months of a season and certainly most of values were above 100 than I divide the sum with number of month (typical method of average/mean) because the range of fractional snow cover should 0-100.
So, the mean value is representing monthly mean rather than season, so we can calculate seasonal fractional snow cover?
NOTE; I am using MATLAB for this analysis and data set is MOD10C2 ( MODIS monthly fractional snow cover)
I have white wasabi meristems that are not dead but they are not green and shooting either. Meristems were placed on media with streptomycin for 5 days under darkness to reduce phenolic compound exudate and endogenous bacteria. 30% remained green chlorophyll 70% white as snow. The good news is there was no phenolic exudate and no bacteria.
The bad news is that there is no growth.
Can anyone explain why this is so and can this be reversed to show green again.
We try to estimate maximal snow depth in selected remote locations on few mountains, far from meteorological stations (gauges).
I wonder how to estimate as accurately as possible "near maximal snow depth" (probably toward the end of January or early February) in these remote areas? Can Sentinel 2 or kind of SAR provide some help, or maybe some other ideas (Landsat etc)? I suppose that the resolution must be better than 100 m as the small localities for which the data is needed. Thank you for any ideas.
Atmospheric phase is one of the components of the total InSAR phase. In most cases, it is ignored. However, for studies aiming at high precision and reliability, it should be removed. Currently, I am working on estimating snow depth and snow water equivalent using D-InSAR wherein the study area comprises of complex mountainous terrains along with forests (North West Himalayas). So I think atmospheric phase removal would increase the quality of the obtained results.
Isn't temperature fall and down related to geological and astronomical dynamics rather than anthropogenic!
My latest results indicate that conceptual models can be improved by warming rain/snow thresholds for higher cloud bases, or lower relative humidity measured at the surface. Both of which are proxy data indicating a drier atmosphere the hydrometer must fall through.
I have found sources that state the microphysics are different in saturated and unsaturated environments, which gets me about 75% of the way to explaining the findings.
What I need to finish the theory is an article that support latent heat exchange rates (evaporation and sublimation) nearing or exceeding sensible heat exchange rates (melting) to allow snow to occur at warmer and drier surface conditions.
If there is a different explanation, I could look into changing the theory.
If I don't find something by the end of the month I could leave it somewhat unexplained, which is not desirable.
Terrestrial water storage (TWS) changes are calculated using GRACE data given as TWS = Surface water + Soil water + Snow.
How to calculate TWS from CMIP data ? what parameters can i use from GCMs?
Is there any possibility to calculate TWS from water balance equation?
Many places in Norway we are now getting large amounts of snow on the roofs of our houses. It would be interesting to read articles (if they exist) of alternative ways of getting snow down from the roof. Special focus on safety and effectiveness is of extra interest.
Dear In the article entitled "Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images" the normalization of the backscattering coefficient in Sentinel 1C images for the 30 degree angle of incidence was performed, since the raw image can range from 18 to 46 degrees. if there is any procedure that can be done in the Sentinel Application Platform (SNAP) or if the sigma-0 file generated in the processing is already normalized data at a certain angle of incidence. Thanks for listening.