The Climate Prediction Center (CPC) Morphing (CMORPH) technique produces global estimates of hourly precipitation and those data are available in real time at this website. (Other JPSS/ABI-related flood products are available there as well). CMORPH uses microwave data from Polar Orbiters to estimate rainfall. Infrared information is related to those microwave data estimates, and those infrared data (from ABI, AHI, etc) are used to estimate precipitation during times when microwave observations are not present. This morphing technique is thus different from other morphing techniques for Total Precipitable Water (TPW) such as MIMIC that use model data to move quasi-conserved moisture fields to observational data voids. (Training on MIMIC TPW fields).

Hourly and Daily precipitation totals are available at the website. The animation above shows annual precipitation over the course of 6 days. Daily precipitation on 27 August, for example (link), shows the heavy rain accompanying land-falling Hurricane Laura. In the animation, the rains with that system move north to the mid-Mississippi River before being shunted eastward.

Hourly Precipitation, shown below from 12-15 UTC on 3 September 2020, allows for enhanced situational awareness with respect to heavy (or light) rains.

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1-minute Mesoscale Domain Sector GOES-17 (GOES-West) Shortwave Infrared (3.9 µm) images (above) showed a number of thermal anomalies — clusters of hot pixels, yellow to red enhancement — associated with wildfires that developed across parts of Montana on 02 September 2020. The SPC Fire Weather Outlook had highlighted critical to extreme fire weather conditions over much of that region, which included strong winds both ahead of and behind a cold front that was moving southward across Montana. As winds shifted to northerly in the wake of the cold frontal passage, visibility was reduced to 6 miles at Billings (KBIL) as smoke from a fire (located approximately 40 miles to the north) began to drift over that location.

A 4-panel comparison of GOES-17 “Red” Visible (0.64 µm), GOES-17 Shortwave Infrared, GOES-16 (GOES-East) Fire Power and GOES-17 Fire Temperature Red-Green-Blue (RGB) images (below) provided a closer view of the Huff Fire — which, fanned by northwest winds gusting as high as 59 mph, made a rapid run toward the southeast and prompted an evacuation of residents in Jordan.

A time-matched comparison of Shortwave Infrared images from Suomi NPP (overpass map) and GOES-17 at 2042 UTC is shown below. The higher spatial resolution of the VIIRS instrument on Suomi NPP (375 meters) more accurately detected the shape and areal extent of the fire at that time, compared to the 2 km spatial resolution (at the satellite subpoint) of the ABI instrument on GOES-17. Northwest winds were gusting to  knots (51 mph) at the Jordan airport.

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NOAA and CIMSS are developing a product that uses a deep-learning model to recognize complex patterns in weather satellite imagery to predict the probability of lightning in the short term. Deep learning is a branch of machine learning based on artificial neural networks, which have the ability to automatically learn targeted features in the data by approximating how humans learn.

A convolutional neural network (CNN) was trained on over 23,000 images of GOES-16 Advanced Baseline Imager (ABI) data to predict the probability of lightning, within any given ABI pixel, in the next 60 minutes. The CNN was trained using 118 days of data collected between May and August of 2018. Images of GOES Geostationary Lightning Mapper (GLM) flash-extent density (created with glmtools) were used as the source of lightning observations. Note that GLM is an optical sensor that observes both in-cloud and cloud-to-ground lightning.

The CNN currently uses four ABI channels: band 2 (0.64-µm), band 5 (1.6-µm), band 13 (10.3-µm), and band 15 (12.3-µm). Bands 2 and 5 are only utilized under sunlit conditions. Utilization of additional channels and time sequences of images is under investigation. The model uses a semantic image segmentation architecture to assign the probability of lighting in the next 60 minutes to each pixel in the image. The model is very computationally efficient, only needing 30 seconds to process the ABI CONUS domain and 3 seconds to process an ABI mesoscale domain using multithreading on a 40-CPU linux server.

Currently, the model only utilizes satellite radiances. Thus, it can be applied to nearly any spatial domain covered by the ABI or an ABI-like sensor (e.g. AHI). Based on near-real-time testing, the model routinely nowcasts lightning initiation with 10-30 minutes of lead-time. We expect the skill and lead-time will increase as new predictors (e.g. more ABI fields, NWP, radar where available) are added to the model.

Below are a sampling of recent examples. The base images are 0.64-µm reflectance, with GLM-derived flash-extent density overlaid as filled semi-transparent polygons. The flash-extent density is the accumulated number of flashes within the previous 5 minutes. The CNN-derived probabilities are displayed as contours at select levels (near-real-time output is available through RealEarth).

The overall objective is to improve lightning nowcasts in support of aviation, mariners, and outdoor events/activities. Beyond improving the CNN, our work will focus on packaging the output into actionable information for forecasters and other decision makers.

A cold front in Iowa

 

Thunderstorm development on sea-breeze boundaries in Florida and the Bahamas

 

Diurnally and orographically forced storms in the Southwest U.S. and Rocky Mountains

 

Storms in central Oklahoma, on the edge of Hurricane Laura’s cloud shield

 

A couple of examples over the Northeast U.S.

 

A boundary of convection on the southern bank of Lake Ontario

 

Storms in a warm sector in IL/IN/OH, perhaps along an outflow boundary

 

Southeast U.S. offshore region

The background image in this example transitions from 10.3-µm brightness temperature to 0.64-µm reflectance, while the flash-extent density enhancement also changes, in an attempt to enhance contrast.

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1-minute Mesoscale Domain Sector GOES-16 (GOES-East) “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images — with and without an overlay of GLM Flash Extent Density (above) showed the rapid development of thunderstorms along a cold front across eastern South Dakota on 30 August 2020. One particularly well-defined and long-lived Enhanced-V signature with an Above-Anvil Cirrus Plume (reference | VISIT training) was seen in the Visible and Infrared imagery, which extended northeastward from the core of a thunderstorm where large hail and tornadoes were occurring.

1-minute GOES-16 Visible images with time-matched plots of SPC Storm Reports are displayed above, with the corresponding Infrared images below.

There were some overshooting tops which exhibited infrared brightness temperatures as cold as -72ºC — a plot of 00 UTC rawinsonde data from Aberdeen, South Dakota (below) indicated that this was about 7ºC colder than the tropopause temperature of -66.1ºC.

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