An on-screen navigator can also be used to pan, zoom, rotate, and tilt the view.In this tutorial, you'll navigate a map and a scene using the Explore tool and the navigator. You'll also learn how to link views so your map and scene pan, zoom, and rotate together.Estimated time: 15 minutesSoftware requirements: ArcGIS Pro

Lesson: Navigate a 3D world, add data from online, and share your results.In this lesson, you'll explore the world with ArcGIS Earth, a desktop application that displays 2D and 3D data from around the globe. You'll add map layers from files and ArcGIS Living Atlas of the World, then pan, zoom, and tilt to view them. Your exploration will encompass Nairobi, Mount Fuji, California, and several other locales. When finished, you'll share your map data via email.This lesson was last tested on March 29, 2022 using ArcGIS Earth 1.14. If you're using a different version of ArcGIS Earth, you may encounter different functionality and results.RequirementsArcGIS EarthUser, Publisher, or Administrator role in an ArcGIS organization for ArcGIS Online (get a free trial) or ArcGIS Enterprise (see configuration details)

Map with solar irradiation and PV power potential in Venezuela. The GIS data stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated. Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential kWh/kWp GHI – Global horizontal irradiation kWh/m2 DIF – Diffuse horizontal irradiation kWh/m2 GTI – Global irradiation for optimally tilted surface kWh/m2 OPTA – Optimum tilt to maximize yearly yield ° DNI – Direct normal irradiation [kWh/m2]

Map with solar irradiation and PV power potential in Marshall Islands. The GIS data (AAIGRID and GEOTIFF) stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated. Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential kWh/kWp GHI – Global horizontal irradiation kWh/m2 DIF – Diffuse horizontal irradiation kWh/m2 GTI – Global irradiation for optimally tilted surface kWh/m2 OPTA – Optimum tilt to maximize yearly yield ° DNI – Direct normal irradiation [kWh/m2].

description: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. ### License Info This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.; abstract: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. ### License Info This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Map with solar irradiation and PV power potential in Chad. The GIS data (AAIGRID and GEOTIFF) stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated.

Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential [kWh/kWp] (2) GHI – Global horizontal irradiation [kWh/m2] (3) DIF – Diffuse horizontal irradiation [kWh/m2] (4) GTI – Global irradiation for optimally tilted surface [kWh/m2] (5) OPTA – Optimum tilt to maximize yearly yield [°] (6) DNI – Direct normal irradiation [kWh/m2].

Map with solar irradiation and PV power potential in Brazil. The GIS data (AAIGRID and GEOTIFF) stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated.

Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential [kWh/kWp] (2) GHI – Global horizontal irradiation [kWh/m2] (3) DIF – Diffuse horizontal irradiation [kWh/m2] (4) GTI – Global irradiation for optimally tilted surface [kWh/m2] (5) OPTA – Optimum tilt to maximize yearly yield [°] (6) DNI – Direct normal irradiation [kWh/m2].