Wednesday Feb 28, 2024

SMAP-3

SMAP-3

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--Computers are managed by software. Software may be divided into three categories: system, utility, and application.

What is the difference between download and install?

--The act of "downloading" a file is distinct from "installing" it. Instructions to utilize the downloaded data to modify your computer are "installing" the file. The file does not alter or be updated if installation is not performed.

What is software used for?

--Software is a collection of instructions, data, or computer programs used to run machines and carry out certain activities. It is the antithesis of hardware which refers to a computer external components. A device running programs, scripts, and applications are collectively referred to as "software" in this context.
SMAP-3
The stock market has different cycles, such as, four-year presidential cycle, fiscal reporting cycles. In addition, some cycles are defined by intrinsic characteristic properties of the system. The stock market performance curve can be considered as a sum of the cyclical functions with different periods and amplitudes. It is not easy to analyze the repetition of typical patterns using a simple chart analysis because cycles mask themselves – sometimes they overlap to form an abnormal extremum or offset to form a flat period. Stock Market Analyzer-Predictor SMAP-3 is able to extract basic cycles of the stock market (indexes, sectors, or well-traded shares) and to predict an optimal timing to buy or sell stocks. Its calculation mainly based on extracting basic cyclical functions with different periods, amplitudes, and phases from historical quote curve. To detect correctly major cycles, the historical price data are transformed from time domain to frequency domain (spectrum). At the beginning SMAP-3 does a simulation (back testing) of forecast on relevant past data in order to estimate the accuracy of prediction with certain parameters. Then it calculates the prediction for the time period forward using internal optimized parameters. Using back testing also allows user to find an optimal time frame. By selecting data with different historical periods, user can identify the major cycles, which have a dominant effect in a particular time frame. To build an extrapolation (predicted curve), SMAP-3 uses the following two-step approach: (1) applying spectral (time series) analysis to decompose the curve into basic functions, (2) composing these functions beyond the historical data. SMAP-3 also enables finding optimal timing to buy/sell by analyzing month of year, day of month, and day of week (the calculation is based on statistical analysis). SMAP-3 has a user-friendly easy-to-use interface. This software is intended for investors with a basic knowledge in stock investing.
his enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scal
  • As part of the algorithm changes, the following data fields were added: bulk_density, clay_fraction.
  • The baseline algorithm (SCA-V) remains unchanged.
  • Improved aggregation of values in input ancillary data, e.g. roughness, soil texture, NDVI. The fix has negligible impacts on retrievals estimated to be of recommended quality.
  • The version 3.0 70km SMAP-SSS level 3, 8-Day running mean gridded product is the third release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include: Use of the version 4 L1B SMAP RFI filtered antenna temperatures; Implementation of the geophysical model function from Aquarius version 5 adapted to SMAP; Use of the near real time CCMP wind speed and direction data as ancillary input, and inclusion of IMERG rain rate for the atmospheric liquid cloud water correction and rain flagging; Improved computation of antenna weighted land fraction gland and enhanced correction for land radiation intrusion from antenna sidelobes; Improved SMAP mesh antenna emissivity settings with empirical adjustments to the JPL thermal model. Users should note that significant degradation in the performance is observed if the gain weighted land fraction gland exceeds 1 percent. Because of that, observations with gland greater than 0.8 percent are not used in the Level 3 processing. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent and gridded at 0.25degree x 0.25degree with an approximate spatial feature resolution of 70km. For most open ocean applications, the 70-km products are the best to use as they have significantly lower noise than the related 40-km V3.0 products. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.

    Abstract: Satellite sensor systems for soil moisture measurements have been continuously evolving.
    The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard.
    Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region
    of North America has originated from evaluation studies using in situ data from the U.S. Department
    of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network
    and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for
    this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture
    in this region, which is characterized by a relatively large soil moisture variability and is one of
    the least studied regions. In this work, SMAP Level 2-4 soil moisture products: SMAP/Sentinel-1
    L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture
    (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU)
    are evaluated over the southern portion of the Great Lakes region using in situ measurements from
    Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased
    root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture
    estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3
    (0.037 m3 m−3
    ) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE
    values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below
    0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network.
    The average ubRMSE values are ~0.05-0.06 m3 m−3
    for high-resolution SPL2SMAP_S soil moisture
    retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture
    retrievals exceeding thaThe PI-produced JPL V5.0 SMAP Sea Surface Salinity (SSS) and extreme winds Level 3 (L3) standard datasets is based on the JPL Combined Active-Passive (CAP) algorithm applied to data from the NASA Soil Moisture Active Passive (SMAP) observatory. For science applications, use of the standard products is strongly advised.

    They also recommend the use of the V5.0 forward stream data over the prior version, which is deprecated and will be retired within the next 6 months. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. Data begins on 1 April 2015 and is ongoing. Datasets comprising this release include the L2B orbital data and two L3 mapped, global coverage salinity datasets: an 8-day running mean dataset based on the repeat orbit of the SMAP mission, along with a monthly average dataset. The L3 data are gridded at 0.25 x 0.25. The spatial resolution of all four datasets is approximately 60 km.

    The JPL SMAP-SSS CAP V5.0 datasets are described and discoverable via the PO.DAAC data portal.

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