“All weather is local” (and can change quickly) and satellites allow the monitoring of a range of phenomena on fine time and space scales. This includes, but not limited to air quality, aviation, convective initiation, the cryosphere, fire detection, fog detection, heavy rain, lightning, marine weather, tropical cyclones, volcanic activity, winter weather and more.

There are a number of online resources to better understand what the weather is going to be like at a given place and time. What resources to look at will depend on the location and length of the forecast ahead of time. For example, if the date is too far in the future, then climatology may be the best tool, while satellite imagery may be the best for a short-term cloud cover “nowcast”.

The best place to start might be your local NOAA NWS Weather Forecast Office web page (eg, MKX), reachable via a national map.

Climatology

Years or Months before the event one is interested in, climatology shows what has happened on this date in the past. It’s best to look at not only the averages (for temperatures and precipitation), but also the extremes. Click on “Climate Graphs”. Or visit the CPC page.

Click on “Normals” or “Records”, then chose the City and month of interest.

Long-term Outlooks

There are a number of longer-term outlooks for temperature and precipitation, including 3 months ahead of time, as was as weeks ahead of time. Start by clicking the “Outlooks” tab.

Within a Week

A week ahead of a given event is covered by global forecast models. These models leverage many observations, including satellite observations. A few days ahead of a given event is covered by regional forecast models. These models leverage many observations, including satellite observations. In fact, satellites are the backbone of NWP observations. Guidance from both of these can be found on the WFO home page, or the hourly-weather graph.

More on how to read the hourly weather graphs from the NWS.

Same day

The closer to the time of the event, the more one can view current weather, including satellite images. This includes integrated products like the research probability of severe weather or lightning, fires products.

More on LightningCast.

Many states have a “mesonet” of surface observations, including in Wisconsin (maps).

More satellite imagery from NOAA/STAR, UW/SSEC, UW/GeoSphere and CSU/CIRA.

Time of the event

While “looking out the window” is good advice, even better might be to go outside and check. Of course the weather can change quickly, so keep an eye on rapidly changing conditions. One can also check out a number of roof top cameras, for example those at UW/SSEC and AOS.

View only this post Read Less

The past few days have brought devastating rain and flooding to the Australian state of Queensland, in the country’s northeast. The community of Paluma received more than 1.4 meters (4.5 feet) of rain over the weekend, and damage to roads and bridges carried out by the torrents of water have effectively cut off parts of northern Queensland from the rest of the country. Thousands of residents have been told to evacuate While the rains have started to abate, the damage will be felt for many months.

The Blended Total Precipitable Water (TPW) product provides a quantitative assessment of just how much water was present in the atmosphere. TPW is measured with microwave instruments, and thus can be measured even in the presence of clouds. However, microwave sounders are only found on polar-orbiting satellites which means that a particular location is going to be observed by several different satellites over the course of a day. Satellites from NOAA, NASA, the US Department of Defense, EUMATSAT, and others all are making TPW observations operationally. The Blended TWP product corrects across all of these instruments with their unique traits to create a globally continuous product that updates at a temporal resolution that is far finer than can be addressed by a single satellite.

 This Blended TPW loop shows the evolution of the TPW from 0300 UTC to 1700 UTC on 31 January 2025. It is clear that northeastern Australia is beset with moisture-laden air, as TPW values are easily over 70 mm.  

The MIMIC-TPW2 hourly composite product is created from retrievals using AMSU-B and MSU onboard NOAA-18, NOAA-19, Metop-A and Metop-B; it also uses retrievals from ATMS onboard the Suomi-NPP, NOAA-20 and NOAA-21 polar orbiting satellites. In a 3-day animation of MIMIC-TPW2 (below), a cyclonic gyre of high TPW (with values often exceeding 70 mm, denoted by lighter shades of violet) is evident in the vicinity of far northern Queensland.

The sounding from Willis Island, about 280 miles east of the Queensland shore in the Coral Sea, affirms how moist the air was. In effect, the atmosphere was saturated throughout the whole depth of the troposphere.

The ensuing convection was extremely vigorous, with cloud top temperatures dropping below -80 C as can be seen in the zoomed-in loop of the 10.4 micron window channel from the geostationary Himawari-9 satellite. Numerous overshooting tops are present, and given the high altitude of the tropical tropopause, those clouds are topping out at over 46,000 feet (14 km). With very little lateral propagation of these storms, it’s easy to see why the rainfalls were so substantial.

Image sources above include: CIRA RAMMB Slider, University of Wyoming Sounding Archive, CIMSS Tropical Cyclones

_________

A longer animation of Himawari-9 Infrared images (below) revealed that cloud-top 10.4 µm infrared brightness temperatures were occasionally -90ºC or colder (yellow-to-gray pixels embedded within darker purple regions) — for example, near Cairns YBCS at 1030 UTC.

A Himawari-9 Rain Rate derived product from the RealEarth site is shown below — Extreme rates (shades of red to violet) were associated with some of the convective clusters.

View only this post Read Less

5-minute PACUS Sector GOES-18 (GOES-West) True Color RGB images from the CSPP GeoSphere site (above) displayed the bright white signature of fresh snow cover on the summits of Mauna Kea and Mauna Loa on the Big Island of Hawai`i on 1st February and 2nd February 2025. This snowfall occurred as a strong storm brought severe thunderstorms and heavy rainfall to parts of the island chain on 30-31 January.

30-meter resolution Landsat-8 “Natural Color” RGB imagery from the RealEarth site (below) provided a more detailed view of the snow cover (brighter shades of cyan) on 1st February.

===== 3rd February Update =====

Toggles between Sentinel-2 True Color RGB images (source) of Mauna Kea (above) and Mauna Loa (below) showed the 2 summits before snowfall (on 29th January) and after snowfall (on 3rd February).

View only this post Read Less

The ProbSevere LightningCast version 1 (v1) model uses machine learning and GOES-R Advanced Baseline Imager (ABI) data to predict the probability of lightning in the next 60 minutes. This version will be operational at NOAA later in 2025.

As research and development continues, a new version of the model (LightningCast v2) adds Multi-Radar Multi-Sensor (MRMS) Reflectivity at -10^oC as a predictor. Reflectivity at -10^oC is well correlated with imminent lightning activity due to its ability to depict hydrometeors in the mixed-phase region of convection (generally 0^oC to -20^oC). Other radar-derived parameters are being investigated as well.

Preliminary results demonstrate that the radar-derived predictor adds value to the problem of short-term lightning prediction, without diminishing the power of the satellite predictors—that is, the model appears to have learned and uses the strengths of each data source for making better predictions.

Here are two recent examples. First, in Ohio and Pennsylvania, warm air advection at 850 and 700 mb forced some elevated but shallow thunderstorms.

A short-term model profile from the NAM 3-km model also shows the elevated but abbreviated extent of CAPE.

The first animation below is from LightningCast v1. The shallow nature of the storms and the thick layer of ice clouds above the convection obscures key signatures for the satellite-only model, resulting in poor probabilistic guidance.

The animation below uses the LightningCast v2 model. While not a cure-all, the reflectivity at -10^oC clearly helps the model provide better guidance to lightning, albeit with little lead time to lightning initiation in this case.

The day prior, weak 850 mb warm air advection forced convective development in northeast Colorado and southwest Nebraska. The thermal profile appeared to be too cold to generate much CAPE or lightning.

However, from a satellite perspective, the convection certainly looks like it could produce lightning. Thus, the LightningCast v1 output shows high probabilities of lightning (animation below).

But the reflectivity at -10^oC predictor generally has convective cores of only 25-30 dBZ. Typically 35-40 dBZ is needed for lightning at this isotherm.

The radar predictor helped reduce the false alarm predictions of lightning markedly. See the animation of LightningCast v2 predictions below.

Overall, we’ve found that LightningCast v2 improves the critical success index over the contiguous U.S. (CONUS), while not harming predictions outside of the CONUS. In the image below, red regions show improvement of v2 over v1, whereas blue regions show degradation of performance in v2, with respect to v1 (note that this is a limited sample).

An assessment of lead time to lightning initiation (LI) has shown that rather than diminishing lead time to LI, LightningCast v2 actually appears to increase lead time to LI over the CONUS by a small amount. Work is on-going to quantify how much ABI predictors alone increase lead time to LI ahead of a radar-only lightning nowcasting model. We hope to have forecasters evaluate LightningCast v2 at the 2025 Hazardous Weather Testbed.

View only this post Read Less