This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the nlme and lme4 R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

• Has there been an increasing or decreasing trend in the response variable over time?

• Can we forecast conditions in the future?

  Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

• Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

• Data gaps: Data are not collected at regular intervals, necessitating interpolation between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

• Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

• Heteroscedasticity: The variance of the time series is not constant over time.

• Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

  Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

Cloud coverage remains a key issue for researchers working with satellite data. Accurate reconstruction of measurements obstructed by cloud can enhance the usefulness of satellite databases for identifying trends and changes in various environments. In this work, we develop, train and test a bidirectional long short-term memory (BiLSTM) model with a custom temporal penalty layer for filling gaps in sea surface temperature (SST) images acquired by the Himawari- 8 satellite. The proposed model showed strong performance, achieving a per-image MAE of 0.1193◦C and per-image RMSE of 0.0985◦C. Our model is also shown to outperform previous state-of-the-art literature. Overall, this work shows that our BiLSTM algorithm is an effective tool for gapfilling cloud-affected SST data. Citation: S. Baker, Z. Huang and B. Philippa, "Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-10, 2023, Art no. 4204310, doi: 10.1109/TGRS.2023.3273575

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Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Eurasia, Africa and Oceania. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.

LANDFIRE's (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

LANDFIRE's Annual Disturbance products track how landscapes change across space and time on an annual basis. The Annual Disturbance (Dist) product identifies satellite-detected areas larger than 4.5 hectares (11 acres) that underwent natural or human-caused changes within a specific year (for Dist23, October 1, 2022 – September 30, 2023), or represent fire activity/field treatments as small as 80 square meters. While creating the Annual Disturbance product a variety of data sources are leveraged. 1) National fire mapping programs: This includes information from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), which offer severity information for fire-caused disturbances. 2) Agency-reported events: There are 18 designated classes for contributed polygon "Event" types such as disease, insects, development, harvest, etc. that are reported by government agencies for inclusion into the disturbance product. 3) Remotely sensed imagery: Harmonized Landsat Sentinel (HLS) satellite images offer a comprehensive-uninterrupted view of the landscape covering all lands, public and private, to fill in the gaps inherent in the previous data sources. These data are reviewed and edited by a team of image analysts to ensure and maintain high quality standards. To create the LF Annual Disturbance product, individual Landsat scenes are stacked and made into composites representing the 15th, 50th, and 90th percentiles of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year and the two prior years serves as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are most commonly caused by differences in annual or seasonal phenology, artifacts in the image composites, or difficult to map classes such as wetlands and grasses. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from modeling are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed as fire by using Burned Area (BA) Level-3 science products derived from Landsat 8 and 9. BA data is only available in the lower 48 states (CONUS). Causality information assigned to annual disturbance products are prioritized by source, with the highest priorities reserved for fire mapping program data (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, satellite image-based change. Severity is assigned directly from fire program data. For events and satellite-detected change, severity is derived from pre- and post-burn standard deviation values of the differenced Normalized Burn Ratio (dNBR). When mapping the LF Annual Disturbance product, the start date is utilized for disturbances from fire program data whereas all other disturbances utilize the end date.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat7 SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

Data Set Overview
=================
This data set consists of electric field spectrum analyzer data
from the Voyager 1 Plasma Wave Subsystem obtained during the
entire mission. Data after 2013-12-31 will be added to the archive on subsequent volumes. The data set encompasses all spectrum
analyzer observations obtained in the cruise mission phases
before, between, and after the Jupiter and Saturn encounter phases
as well as those obtained during the two encounter phases.

The Voyager 1 spacecraft travels from Earth to beyond 100 AU over     
the course of this data set. To provide some guidance on when      
some key events occurred during the mission, the following table     
is provided.                               

Date     Event                            
1977-09-05  Launch                            
1979-02-28  First inbound bow shock crossing at Jupiter         
1979-03-22  Last outbound bow shock crossing at Jupiter         
1980-11-11  First inbound bow shock crossing at Saturn          
1980-11-16  Last outbound bow shock crossing at Saturn          
1981-02-20  10 AU                            
1983-08-30  Onset of first major LF heliospheric radio event       
1984-06-19  20 AU                            
1987-04-08  30 AU                            
1990-01-09  40 AU                            
1992-07-06  Onset of second major LF heliospheric radio event      
1992-10-10  50 AU                            
1995-07-14  60 AU                            
1998-04-18  70 AU                            
2001-01-25  80 AU                            
2002-11-01  Onset of third major LF heliospheric radio event       
2003-11-05  90 AU                            
2004-12-16  Termination shock crossing                  
2006-08-16  100 AU                            
2009-05-31  110 AU                            
2012-03-16  120 AU                            
2015-01-01  130 AU                            

Data Sampling
=============
This data set consists of full resolution edited, wave electric
field intensities from the Voyager 1 Plasma Wave Receiver spectrum
analyzer obtained during the entire mission. For each time
interval, a field strength is determined for each of the 16
spectrum analyzer channels whose center frequencies range from 10
Hertz to 56.2 kiloHertz and which are logarithmically spaced in
frequency, four channels per decade. The time associated with
each set of intensities (16 channels) is the time of the beginning
of the scan. The time between spectra in this data set vary by
telemetry mode and range from 4 seconds to 96 seconds. During
data gaps where complete spectra are missing, no entries exist in
the file, that is, the gaps are not zero-filled or tagged in any
other way. When one or more channels are missing within a scan,
the missing measurements are zero-filled. Data are edited but not
calibrated. The data numbers in this data set can be plotted in
raw form for event searches and simple trend analysis since they
are roughly proportional to the log of the electric field
strength. Calibration procedures and tables are provided for use
with this data set; the use of these is described below.

For the cruise data sets, the timing of samples is dependent upon     
the spacecraft telemetry mode. In principle, one can determine      
the temporal resolution between spectra simply by noting the       
difference in time between two records in the files. In some       
studies, more precise timing information is necessary. Here, we     
describe the timing of the samples for the PWS low rate data as a     
function of telemetry mode.                        

The PWS instrument uses two logarithmic compressors as detectors     
for the 16-channel spectrum analyzer, one for the bottom (lower      
frequency) 8 channels, and one for the upper (higher frequency) 8     
channels. For each bank of 8 channels, the compressor          
sequentially steps from the lowest frequency of the 8 to the       
highest in a regular time step to obtain a complete spectrum. At     
each time step, the higher frequency channel is sampled 1/8 s       
prior to the lower frequency channel so that the channels are       
sampled in the following order with channel 1 being the lowest      
frequency channel (10 Hz) and 16 being the highest (56.2 kHz): 9,     
1, 10, 2, 11, 3, ... 15, 7, 16, 8. The primary difference        
between the various data modes is the stepping rate from one       
channel to the next (ranging from 0.5 to 12 s, corresponding to      
temporal resolutions between complete spectra of 4 s to 96 s).      

In the following table, we present the hexadecimal id for the       
various telemetry modes, the mode mnemonic ID, the time between      
frequency steps, and the time between complete spectra. We also     
provide the offset from the beginning of the instrument cycle (one    
complete spectrum) identified as the time of each record's time      
tag to the time of the sampling for the first high-frequency       
channel (channel 9) and for the first low-frequency channel        
(channel 1).                               

                Time                   
          Frequency  Between   High Freq. Low Freq.    

MODE (Hex) MODE ID Step (s) Spectra (s) offset (s) offset (s)
01 CR-2 0.5 4.0 0.425 0.4325
02 CR-3 1.2 9.6 1.125 1.1325
03 CR-4 4.8 38.4 0.425 0.4325
04 CR-5 9.6 76.8 0.425 0.4325

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri AWQMN AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri AWQMN AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri AWQMN

• Data Set Overview
• =================

This Data Set consists of Electric Field Spectrum Analyzer Data from the Voyager 1 Plasma Wave Subsystem obtained during the entire Mission. Data after 2013-12-31 will be added to the Archive on subsequent Volumes. The Data Set encompasses all Spectrum Analyzer Observations obtained in the Cruise Mission Phases before, between, and after the Jupiter and Saturn Encounter Phases as well as those obtained during the two Encounter Phases.

The Voyager 1 Spacecraft travels from Earth to beyond 100 AU over the Course of this Data Set. To provide some Guidance on when some Key Events occurred during the Mission, the following Table is provided.

+----------------------------------------------------------------+

| Date | Event |

| 1977-09-05 | Launch | | 1979-02-28 | First inbound Bow Shock Crossing at Jupiter | | 1979-03-22 | Last outbound Bow Shock Crossing at Jupiter | | 1980-11-11 | First inbound Bow Shock Crossing at Saturn | | 1980-11-16 | Last outbound Bow Shock Crossing at Saturn | | 1981-02-20 | 10 AU | | 1983-08-30 | Onset of first major LF Heliospheric Radio Event | | 1984-06-19 | 20 AU | | 1987-04-08 | 30 AU | | 1990-01-09 | 40 AU | | 1992-07-06 | Onset of second major LF Heliospheric Radio Event | | 1992-10-10 | 50 AU | | 1995-07-14 | 60 AU | | 1998-04-18 | 70 AU | | 2001-01-25 | 80 AU | | 2002-11-01 | Onset of third major LF Heliospheric Radio Event | | 2003-11-05 | 90 AU | | 2004-12-16 | Termination Shock Crossing | | 2006-08-16 | 100 AU | | 2009-05-31 | 110 AU | | 2012-03-16 | 120 AU | | 2015-01-01 | 130 AU | +----------------------------------------------------------------+

• Data Sampling
• =============

This Data Set consists of Full Resolution edited, Wave Electric Field Intensities from the Voyager 1 Plasma Wave Receiver Spectrum Analyzer obtained during the entire Mission. For each Time Interval, a Field Strength is determined for each of the sixteen Spectrum Analyzer Channels whose Center Frequencies range from 10 Hz to 56.2 kHz and which are logarithmically spaced in Frequency, four Channels per Decade. The Time associated with each Set of Intensities (sixteen Channels) is the Time of the Beginning of the Scan. The Time between Spectra in this Data Set vary by Telemetry Mode and range from 4 s to 96 s. During Data Gaps where complete Spectra are missing, no Entries exist in the File, that is, the Gaps are not zero-filled or tagged in any other way. When one or more Channels are missing within a Scan, the missing Measurements are zero-filled. Data are edited but not calibrated. The Data Numbers in this Data Set can be plotted in raw Form for Event Searches and simple Trend Analysis since they are roughly proportional to the Log of the Electric Field Strength. Calibration Procedures and Tables are provided for use with this Data Set; the Use of these is described below.

For the Cruise Data Sets, the Timing of Samples is dependent upon the Spacecraft Telemetry Mode. In principle, one can determine the Temporal Resolution between Spectra simply by noting the Difference in Time between two Records in the Files. In some Studies, more precise Timing Information is necessary. Here, we describe the Timing of the Samples for the PWS Low Rate Data as a Function of Telemetry Mode.

The PWS Instrument uses two Logarithmic Compressors as Detectors for the sixteen Channel Spectrum Analyzer, one for the bottom (lower frequency) eight Channels, and one for the upper (higher frequency) eight Channels. For each Bank of eight Channels, the Compressor sequentially steps from the lowest frequency of the eight to the highest in a regular Time Step to obtain a complete Spectrum. At each Time Step, the higher frequency Channel is sampled 0.125 s prior to the lower frequency Channel so that the Channels are sampled in the following order with Channel 1 being the lowest frequency Channel (10 Hz) and Channel 16 being the highest (56.2 kHz): 9, 1, 10, 2, 11, 3, ..., 15, 7, 16, 8. The primary Difference between the various Data Modes is the Stepping Rate from one Channel to the next (ranging from 0.5 s to 12 s, corresponding to the Temporal Resolutions between complete Spectra of 4 s to 96 s).

In the following Table, we present the Hexadecimal ID for the various Telemetry Modes, the Mode Mnemonic ID, the Time between Frequency Steps, and the Time between complete Spectra. We also provide the Offset from the Beginning of the Instrument Cycle (one complete Spectrum) identified as the Time elapsed from the Time Tag of each Record to the Time of the Sampling for the first high-frequency Channel (Channel 9) and for the first low-frequency Channel (Channel 1).

+--------------------------------------------------------------------------------------------------------------------------+

| MODE (Hex) | MODE ID | Frequency Step (s) | Spectra (s) | High Freq. Offset (s) | Low Freq. Offset (s) | Notes |

| 01 | CR-2 | 0.5 | 4.0 | 0.425 | 0.4325 | | | 02 | CR-3 | 1.2 | 9.6 | 1.125 | 1.1325 | | | 03 | CR-4 | 4.8 | 38.4 | 0.425 | 0.4325 | | | 04 | CR-5 | 9.6 | 76.8 | 0.425 | 0.4325 | | | 05 | CR-6 | 12.0 | 96.0 | 0.9275 | 0.935 | | | 06 | CR-7 | | | | | Not implemented | | 07 | CR-1 | 0.5 | 4.0 | 0.225 | 0.2325 | | | 08 | GS-10A | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 0A | GS-3 | 0.5 | 4.0 | 0.425 | 0.4325 | | | 0C | GS-7 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 0E | GS-6 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 16 | OC-2 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 17 | OC-1 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 18 | †CR-5A | 0.5 | 4.0 | 0.425 | 0.4325 | | | 19 | GS-10 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 1A | GS-8 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 1D | †UV-5A | 0.5 | 4.0 | 0.425 | 0.4325 | Same as CR-5A | +--------------------------------------------------------------------------------------------------------------------------+

†In CR-5A and UV-5A, the PWS is cycled at its 0.5 s per Frequency Step or 4 s per Spectrum Rate, but four Measurements are summed onboard in 10-bit Accumulators and these 10-bit Sums are downlinked. On the Ground, the Sums are divided by 4, hence providing, in a sense, 16 s is dropped onboard in order to avoid LECP Stepper Motor Interference.

This data set consists of 4-second edited, wave electric field intensities from the Voyager 2 Plasma Wave Receiver (PWS) spectrum analyzer obtained in the vicinity of the Neptunian magnetosphere. For each 4-second interval, a field strength is determined for each of the 16 spectrum analyzer channels whose center frequencies range from 10 Hertz to 56.2 kiloHertz and which are logarithmically spaced in frequency, four channels per decade. The time associated with each set of intensities (16 channels) is the time of the beginning of the scan. During data gaps where complete 4-second spectra are missing, no entries exist in the file, that is, the gaps are not zero-filled or tagged in any other way. When one or more channels are missing within a scan, the missing measurements are zero-filled. Data are edited but not calibrated. The data numbers in this data set can be plotted in raw form for event searches and simple trend analysis since they are roughly proportional to the log of the electric field strength. Calibration procedures and tables are provided for use with this data set

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The Floor Gap Filling Services market has emerged as a vital sector within the flooring and construction industry, providing essential solutions to enhance the longevity and aesthetics of various flooring types. This service is primarily used to address gaps that can form between floorboards, tiles, or other types o

The NETCDF encompasses monthly time series spans the years 1995-2020 globally at 0.5-degree resolution with gap-free estimates of nine variables, gap-filled using the CLIMFILL (CLIMate data gapFILL) framework [1,2]

The nine variables are:

- surface layer soil moisture from the Climate Change Initiative (CCI) of the European Space Agency (ESA),

- land surface temperature and

- diurnal temperature range from the Moderate Resolution Imaging Spectroradiometer (MODIS),

- precipitation from the Global Precipitation Measurement (GPM),

- terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE),

- ESA-CCI burned area,

- ESA-CCI snow cover fraction,

- two-meter temperature and precipitation from the Climate Research Unit (CRU).

Note: this dataset is only validated and tested for the use cases in the accompanying study (DOI to come). Please have caution using the data for analysis that might include trends in high latitude, in regions where the variable has high fraction of missing values, or in mountainous regions.

References:

[1] Bessenbacher, V., Seneviratne, S.I. and Gudmundsson, L. (2022): CLIMFILL v0.9: a framework for intelligently gap filling Earth observations. Geosci. Model Dev., 15, 4569–4596, 2022 https://doi.org/10.5194/gmd-15-4569-2022

[2] https://github.com/climachine/climfill/releases/tag/1.0

Success.ai’s Education Industry Data for E-Learning & Education Management Experts Worldwide offers a reliable and comprehensive dataset tailored for businesses and institutions looking to connect with professionals in the global education sector. Covering e-learning innovators, education managers, and administrative leaders, this dataset provides verified contact details, including work emails, phone numbers, and professional insights.

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• Data Set Overview

• =================

• Version 1.1

• ===========

This Version 1.1 Data Set replaces the Version 1.0 Data Set (DATA_SET_ID=VG2-J-PWS-2-SA-4.0SEC) previously archived with the PDS. Changes to this Version include the Upgrade of the associated Labels and Templates to PDS Version 3 Compliance.

• Data Set Description
• ====================

This Data Set consists of 4 s edited, Wave Electric Field Intensities from the Voyager 2 Plasma Wave Receiver Spectrum Analyzer obtained in the Vicinity of the Jovian Magnetosphere. For each 4 s Interval, a Field Strength is determined for each of the sixteen Spectrum Analyzer Channels whose Center Frequencies range from 10 Hz to 56.2 kHz and which are logarithmically spaced in Frequency, four Channels per Decade. The Time associated with each Set of Intensities (sixteen Channels) is the Time of the Beginning of the Scan. During Data Gaps where complete 4 s Spectra are missing, no Entries exist in the File, that is, the Gaps are not Zero-filled or Tagged in any other way. When one or more Channels are missing within a Scan, the missing Measurements are Zero-filled. Data are edited but not calibrated. The Data Numbers in this Data Set can be plotted in raw Form for Event Searches and simple Trend Analysis since they are roughly proportional to the Log of the Electric Field Strength. Calibration Procedures and Tables are provided for use with this Data Set; the use of these is described below.

• Use of Voyager PWS Calibration Tables
• =====================================

The Voyager PWS Calibration Table is given in an ASCII Text File named SA_CL_4S.TAB (for Voyager 2). This provides Information to convert the uncalibrated "Data Number" Output of the PWS sixteen-channel Spectrum Analyzer to calibrated Antenna Voltages for each Frequency Channel. Following is a brief Description of these Files and a Tutorial in their application.

Descriptive Headers have been removed from the Calibration Table File. The Columns included are IDN, ICHAN01, ICHAN02, ICHAN03, ICHAN04, ICHAN05, ICHAN06, ..., ICHAN16.

The first Column lists an uncalibrated Data Number followed by the corresponding Value in calibrated Volts for each of the sixteen Frequency Channels of the PWS Spectrum Analyzer. Each Line contains Calibrations for successive Data Number Values ranging from 0 through 255. (Data Number 0 actually represents the Lack of Data since the Baseline Noise Values for each Channel are all above that.)

A Data Analysis Program may load the appropriate Table into a Data Structure and thus provide a simple Look-up Scheme to obtain the appropriate Voltage for a given Data Number and Frequency Channel. For example, the following VAX FORTRAN Code may be used to load a Calibration Array for Voyager 2 PWS:

• real*4 cal(16,0:255)
• open(unit=10,file='SA_CL_4S.TAB'status='old') *
• do i=0,255
• read(10,*) idn,(cal(ichan,i),ichan=1,16)
• end do *
• close(10)

Then, given an uncalibrated Data Value idn for the Frequency Channel ichan, the corresponding calibrated Antenna Voltage would be given by the following Array Reference:

• volts=cal(ichan,idn)

This may be converted to a Wave Electric Field Amplitude by dividing by the effective Antenna Length in meters, 7.07 m. That is:

• efield=cal(ichan,idn)/7.07

Spectral Density Units may be obtained by dividing the Square of the Electric Field Value by the nominal Frequency Bandwidth of the corresponding Spectrum Analyzer Channel.

• specdens=(cal(ichan,idn)/7.07)^2/bandwidth(ichan)

Finally, Power Flux may be obtained by dividing the Spectral Density by the Impedance of Free Space in Ohms:

• pwrflux=((cal(ichan,idn)/7.07)^2/bandwidth(ichan))/376.73

Of course, for a particular Application, it may be more efficient to apply the above Conversions to the Calibration Table directly.

The Center Frequencies and Bandwidths of each PWS Spectrum Analyzer Channel for the Voyager 2 PWS are given below:

Voyager 2 PWS Spectrum Analyzer

• Data Set Overview

• =================

• Version 1.1

• ===========

This Version 1.1 Data Set replaces the Version 1.0 Data Set (DATA_SET_ID=VG1-J-PWS-2-SA-4.0SEC) previously archived with the PDS. Changes to this Version include the upgrade of the associated Labels and Templates to PDS Version 3 Compliance.

• Data Set Description
• ====================

This Data Set consists of 4 s edited, Wave Electric Field Intensities from the Voyager 1 Plasma Wave Receiver Spectrum Analyzer obtained in the Vicinity of the Jovian Magnetosphere. For each 4 s Interval, a Field Strength is determined for each of the sixteen Spectrum Analyzer Channels whose Center Frequencies range from 10 Hz to 56.2 kHz and which are logarithmically spaced in Frequency, four Channels per Decade. The Time associated with each Set of Intensities (sixteen Channels) is the Time of the Beginning of the Scan. During Data Gaps where complete 4 s Spectra are missing, no Entries exist in the File, that is, the Gaps are not Zero-filled or Tagged in any other way. When one or more Channels are missing within a Scan, the missing Measurements are Zero-filled. Data are edited but not calibrated. The Data Numbers in this Data Set can be plotted in raw Form for Event Searches and simple Trend Analysis since they are roughly proportional to the Log of the Electric Field Strength. Calibration Procedures and Tables are provided for use with this Data Set; the use of these is described below.

• Use of Voyager PWS Calibration Tables
• =====================================

The Voyager PWS Calibration Table is given in an ASCII Text File named VG1PWSCL.TAB (for Voyager 1). This provides Information to convert the uncalibrated $quot;Data Number$quot; Output of the PWS sixteen-channel Spectrum Analyzer to calibrated Antenna Voltages for each Frequency Channel. Following is a brief Description of these Files and a Tutorial in their Application.

Descriptive Headers have been removed from this File. The Columns included are IDN, ICHAN01, ICHAN02, ICHAN03, ICHAN04, ICHAN05, ICHAN06, ..., ICHAN16.

The first Column lists an uncalibrated Data Number followed by the corresponding Value in calibrated Volts for each of the sixteen Frequency Channels of the PWS Spectrum Analyzer. Each Line contains Calibrations for successive Data Number Values ranging from 0 through 255. (Data Number 0 actually represents the Lack of Data since the Baseline Noise Values for each Channel are all above that.)

A Data Analysis Program may load the appropriate Table into a Data Structure and thus provide a simple Look-up Scheme to obtain the appropriate Voltage for a given Data Number and Frequency Channel. For example, the following VAX FORTRAN Code may be used to load a Calibration Array for Voyager 1 PWS:

• real*4 cal(16,0:255)
• open(unit=10,file='VG1PWSCL.TAB'status='old') *
• do i=0,255
• read(10,*) idn,(cal(ichan,i),ichan=1,16)
• end do *
• close(10)

Then, given an uncalibrated Data Value idn for the Frequency Channel ichan, the corresponding calibrated Antenna Voltage would be given by the following Array Reference:

• volts=cal(ichan,idn)

This may be converted to a Wave Electric Field Amplitude by dividing by the effective Antenna Length in meters, 7.07 m. That is:

• efield=cal(ichan,idn)/7.07

Spectral Density Units may be obtained by dividing the Square of the Electric Field Value by the nominal Frequency Bandwidth of the corresponding Spectrum Analyzer Channel.

• specdens=(cal(ichan,idn)/7.07)^2/bandwidth(ichan)

Finally, Power Flux may be obtained by dividing the Spectral Density by the Impedance of Free Space in Ohms:

• pwrflux=((cal(ichan,idn)/7.07)^2/bandwidth(ichan))/376.73

Of course, for a particular Application, it may be more efficient to apply the above Conversions to the Calibration Table directly.

The Center Frequencies and Bandwidths of each PWS Spectrum Analyzer Channel for each Voyager Spacecraft are given below:

About this dataset

Context

The dataset tabulates the Shade Gap population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Shade Gap.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Shade Gap, PA Population Breakdown by Race
• Shade Gap, PA Non-Hispanic Population Breakdown by Race
• Shade Gap, PA Hispanic or Latino Population Distribution by Their Ancestries

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This data set consists of 4-second edited, wave electric field intensities from the Voyager 2 Plasma Wave Receiver spectrum analyzer obtained in the vicinity of the Saturnian magnetosphere. For each 4-second interval, a field strength is determined for each of the 16 spectrum analyzer channels whose center frequencies range from 10 Hertz to 56.2 kiloHertz and which are logarithmically spaced in frequency, four channels per decade. The time associated with each set of intensities (16 channels) is the time of the beginning of the scan. During data gaps where complete 4-second spectra are missing, no entries exist in the file, that is, the gaps are not zero-filled or tagged in any other way. When one or more channels are missing within a scan, the missing measurements are zero-filled. Data are edited but not calibrated. The data numbers in this data set can be plotted in raw form for event searches and simple trend analysis since they are roughly proportional to the log of the electric field strength. Calibration procedures and tables are provided for use with this data set

These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.