Satellite imagery can provide valuable insight into current atmospheric phenomena, especially in data-sparse areas over the ocean. The identification and monitoring of near-shore, offshore, and high seas fog poses a number of challenges, including limited surface observations, and difficulty distinguishing low-stratus from sea-fog at night. Early detection of fog events provides forecasters with a chance to issue advanced messaging. In this lesson, aimed at operational forecasters with coastal or offshore responsibilities in a tropical area, the learner will practice assessing and monitoring marine fog evolution in a tropical environment using GOES and JPSS satellite products and other resources. Learners work through two case studies set in warm water areas where dense fog events can have important impacts on cruise line operations and other industries. The products and techniques used in the lesson to assess fog can be applied in any warm water environment. The following performance components and skills, techniques, and knowledge requirements from the 2018 WMO Guidelines on Satellite Skills and Knowledge for Operational Meteorologists are addressed in this lesson: 2.3 Identify fog and discriminate between fog and low cloud. 5.1.2 Correctly interpret and appropriately integrate vertical temperature and moisture profiles.

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Blowing snow occurs when snow and ice particles near the surface combine with strong winds and gusts to significantly reduce visibility. Blowing snow can lead to hazardous blizzard conditions that profoundly impact those in their path, posing risks to transportation, infrastructure, and daily life, particularly in Arctic regions. This training provides a conceptual model for blowing snow, reviewing the key ingredients required, and the factors that can impact the visibility. It then leverages this conceptual model to develop your ability to forecast blowing snow and blizzard conditions. In this lesson, you will work through forecast and monitoring shifts for a blowing snow case study focused on Baker Lake (Qamani'tuaq) in Nunavut, Canada. During each of these shifts, you will analyze model forecasts, observations, and other products to assess the risk for blowing snow and blizzard conditions, and predict the details of the blowing snow event.

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The Automated Surface Observing System (ASOS) and Automated Weather Observing Systems (AWOS) are vital components of aviation safety and meteorological data. These automated systems continuously monitor and deliver real time weather data including wind speed and direction, temperature and precipitation. These two systems form part of a nationwide network of weather monitoring stations that ensure users have reliable, real time weather observations and climatological data. In this lesson, you will explore the Automated Surface Observing System (ASOS) and the Automated Weather Observing Systems (AWOS). The lesson will introduce you to the various pieces of equipment found in each system, what some of the key differences and similarities between the systems are, and how to know when maintenance is required. By the end of the lesson, you will have a deeper understanding of the capabilities, data outputs, users, data errors, and maintenance protocols of both ASOS and AWOS.

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Rating curves are an extremely important tool, allowing hydrologists to estimate a river's flow (discharge) by simply measuring the water level (stage) as a surrogate. This provides a practical way to continuously monitor streamflow. The basics section of this lesson focuses on the steps of creating a rating curve. The process begins by making manual measurements of discharge (and stage) over a select period of time and at a range of stages. When building a rating curve, the shape of the curve will depend on factors such as the hydraulic control, as well as the shape, size, slope, and roughness of the stream channel. Rating curves are different for every site. If the river morphology (structure) changes over time due to events such as flooding, vegetation growth, or ice, the rating curves will need to be adjusted or updated. The advanced section of this lesson covers common mistakes made when building rating curves including complexities such as uncertainty, changing controls, and unsteady flow conditions. Because hydraulic controls are an important piece of building rating curves, the recommended prerequisite for this lesson is Streamgage Basics: Controls.

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One of the more challenging forecasts in the Tropics is differentiating when/where there will be heavy rains. Are the predicted storms going to be everyday rains or will they be flood-producing deep convection? There are many products we can use to help with identifying deep convection intensity potential, but there are very few that are made specifically for the Tropics. Your satellite analysis skills can take you only so far into the future for deep convection prediction (nowcasting), but what NWP products are available that are specifically designed for deep convection intensity potential in the Tropics? We will explore those here and, using your satellite analysis skills, compare those NWP outputs to what you already understand from satellite imagery. Let's take the next step in defining better deep convection intensity forecasts. The following performance components and skills, techniques, and knowledge requirements from the 2018 WMO Guidelines on Satellite Skills and Knowledge for Operational Meteorologists are addressed in this lesson: ‭2.2: Identify cumulonimbus clouds, their intensity, organization and stage of development. ‭3.1.1: Intertropical convergence zones, monsoon and trade wind regimes. ‭3.3.2: Convective environments and areas of instability, convective initiation, inhibition and the breakdown of inhibition. ‭5.1.5: Correctly interpret and appropriately integrate total and liquid precipitable water. ‭7.4: Use NWP information to enhance the understanding of the features shown in the satellite images.

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Effective groundwater monitoring is extremely important, especially as freshwater resources are increasingly threatened by climate change. This module was developed in partnership with the World Meteorological Organization (WMO) and is an international adaptation of the “Groundwater Basics: Routine Site Visits” and “Groundwater Basics: Troubleshooting Erroneous Data” lessons designed for the United States Geological Survey (USGS). This module is aimed at an international audience and includes metric units and global examples. Topics covered include: a brief introduction to groundwater and aquifers, the groundwater monitoring process, preparation and packing for field work, datums and basics marks needed at a properly established well, instrumentation for collecting water levels, uncertainty when collecting water levels, and interpretation of groundwater data on hydrographs. This module is a compilation of resources and information. Each section of the module contains an overview of key concepts, opportunities for practice, and printable reference guides (jobs aids).

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This brief lesson focuses on the detection and monitoring of cold-core upper air low pressure systems over the tropical Pacific region using satellite imagery. The benefits of using water vapor imagery and the Airmass RGB in the detection of cold-core upper lows are highlighted while learners compare the effectiveness of NWP data to satellite products. Learners will apply their NWP and satellite interpretation skills as they are challenged to locate upper air low pressure systems.

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Are you new to UNIX command line and need to get a quick tutorial? This is a great place to start. In this lesson, we will explore the command line through some quick exercises that compare command line UNIX to your PC or Mac environment that you are used to. Here you will learn about the basics of UNIX file structures, how to navigate in a UNIX environment, and you’ll get to practice creating, copying and searching for files. All of these basics are then combined into some exercises with wildcards to make all your commands more powerful with a few simple touches. Let's get started!

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The Madden-Julian Oscillation (MJO) can influence weather at subseasonal scales as it moves eastward across the tropical regions of the globe. These influences can extend to midlatitudes including the contiguous U.S., and even as far northward as Alaska. This interactive lesson provides practice assessing the influence of two separate MJO events on temperature and precipitation patterns at U.S. midlatitude locations. You will learn how to use various NOAA Climate Prediction Center (CPC) products including phase-space diagrams with the Realtime Multivariate MJO (RMM) index (Wheeler-Hendon plot), MJO composites, and the Global Tropical Hazards (GTH) outlook.

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Your advisor just dumped a task on your desk without much time to complete the task. You have two different netCDF files that you need to compare to help verify the output from the National Water Model compared to USGS river gage data from Hurricane Harvey (2017). Through this first lesson in the netCDF series (funded by USGS), you will learn to explore metadata and make comparisons between netCDF (Network Common Data Form) files to ensure (Climate and Forecasting) CF conventions are maintained and data are comparable. The main programming language used in this lesson is Python (netCDF4 and xarray libraries used). As a pre-requisite to this lesson, you should be familiar with multi-dimensional data structures by reviewing the lesson from NSF Unidata. This lesson is currently considered provisional pending USGS Office of Quality Assurance approval.

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