[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][nectar_badge display_tag=”label” badge_style=”default” bg_color_type=”custom” bg_color_custom=”#244d75″ text_color=”#ffffff” padding=”medium” border_radius=”10px” display=”block” text=”Module 3: Expert Insights & Global Case Studies”][vc_custom_heading text=”UNEP/NASA climate tracking via AI” use_theme_fonts=”yes” css=””][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_custom_heading text=”UNEP’s Approach: AI in the World Environment Situation Room (WESR)” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes” css=””][/vc_column_inner][/vc_row_inner][vc_column_text css=”” text_direction=”default”]The World Environment Situation Room (WESR) is UNEP’s digital platform that leverages AI to process and visualize data on key environmental indicators such as carbon emissions, wildfire activity, deforestation, glacier melt, and air quality.

AI models within WESR are trained to synthesize satellite imagery, sensor data, and historical datasets, helping UNEP detect trends and generate real-time alerts.

These AI-powered insights support early warning systems, risk forecasting, and evidence-based policymaking at both national and international levels.[/vc_column_text][divider line_type=”No Line” custom_height=”30″][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/2″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”4453″ image_size=”regular” max_width=”100%” max_width_mobile=”default” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”10px” box_shadow=”none” image_loading=”default” fit_to_container=”1″ overflow=”hidden”][/vc_column_inner][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/2″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”4452″ image_size=”regular” max_width=”100%” max_width_mobile=”default” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”10px” box_shadow=”none” image_loading=”default” fit_to_container=”1″ overflow=”hidden”][/vc_column_inner][/vc_row_inner][vc_column_text css=”” text_direction=”default”]This approach aligns with broader development goals by linking technology, climate action (SDG 13), and institutional partnerships (SDG 17).[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”4454″ image_size=”full” max_width=”100%” max_width_mobile=”default” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”10px” box_shadow=”none” image_loading=”default” display_title=”1″ fit_to_container=”1″][divider line_type=”No Line” custom_height=”30″][nectar_responsive_text inherited_font_style=”default” text_direction=”default”]From a Monitoring, Evaluation, and Learning (MEL) perspective, WESR reflects how AI can support continuous, dynamic monitoring and data-driven response systems in the environmental sector.[/nectar_responsive_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_custom_heading text=”NASA’s AI-Powered Climate Tracking” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes” css=””][/vc_column_inner][/vc_row_inner][image_with_animation image_url=”4456″ image_size=”full” max_width=”100%” max_width_mobile=”default” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”10px” box_shadow=”none” image_loading=”default” display_title=”1″ fit_to_container=”1″][divider line_type=”No Line” custom_height=”30″][vc_column_text css=”” text_direction=”default”]Prithvi‑Weather‑Climate: An AI Foundation Model

NASA, in collaboration with IBM Research, developed Prithvi‑Weather‑Climate, a state-of-the-art AI foundation model designed for weather and climate applications. The model is trained on 40 years of MERRA‑2 data, enabling it to capture atmospheric dynamics and extract meaningful patterns across extensive historical datasets.

Key characteristics:

• High-resolution downscaling: Improves spatial detail in forecasts without the need for large-scale computing .
• Adaptability: Capable of being applied globally or regionally without loss of fidelity, offering flexibility across contexts.
• Reduced resource requirements: Less computationally intensive than traditional numerical weather models, lowering barriers to usage

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