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Weather Blog

NASA needs your help

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When was the first time you remember being told that those puffy little clouds on otherwise sunny days are called cumulus clouds? Do you recall the names of other types of clouds? It may be time to brush up, put your cloud knowledge to use, and in the process, help NASA.

The National Aeronautics and Space Administration: Global Learning and Observations to Benefit the Environment (NASA GLOBE) Clouds: Spring Cloud Observations Data Challenge starts Thursday, March 15, and runs through April 15.

cloudy beach

What kind of clouds are those? Is the sky completely covered by clouds? Brush up on your skills and and answer these questions for NASA. Photo credit: Niki Morock

The challenge is open to educators, students, and the general public, which means anyone can participate! The only technology required is access to the GLOBE Program’s data entry options online or the GLOBE Observer App, which is free in the App Store. The rest depends on some basic knowledge of clouds, your own eyes and an unobstructed view of the sky.

I downloaded the GLOBE Observer App today. It was free and easy to set up. You only need five to ten minutes to register and read the instructions once you are logged in. There are step-by-step directions on how to make an observation available on the GLOBE website here.

By participating as a citizen scientist, you are helping “scientists better understand satellite data of our atmosphere.” In other words, you are providing ground evidence to corroborate what the satellite appears to be seeing.

Why is it needed? Satellites see more than just clouds. For example, they can see ice and snow on the surface and smoke. Sometimes, those things look very different from clouds and sometimes they look similar. By collecting data from ground-level observers, scientists add to their understanding of how the satellite sees the clouds and the world below them. The better that understanding is, the better our now-casting and forecasting becomes. The improved knowledge will also help tweak the technology as we put more satellites into orbit.

Don’t worry if you don’t remember the difference between a cirrus cloud and a cumulonimbus cloud. There are tutorials on the GLOBE site, as well as tips and tricks for making a good cloud and sky observation. You don’t need a meteorology degree to be a cloud observer.

Personally, I will do what I can to participate, but in my day-to-day routine, most of my sky views are obstructed. I work in downtown Wake Forest and live on a wooded lot. Still, if the GLOBE Observer app alerts me that a satellite is about to fly over and I am somewhere with a good view of the sky, I will definitely submit an observation. Every little bit of additional, accurate data helps.

Weather Blog

Models need (and will get) improvement

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Two news stories about forecasting models caught my eye this week. The first dealt with a shortfall in the climate models and the second was good news about continuing improvements in our daily forecasting models.

I’ve written before about potential problems with initial data and assumptions in climate forecasting models – the ones used by climatologists to predict our global conditions decades in the future. Like it or not, they are not perfect.

NASA climate map

Credit: NASA, 2015. “NASA global data set combines historical measurements with data from climate simulations using the best available computer models to provide forecasts of how global temperature (shown here) and precipitation might change up to 2100 under different greenhouse gas emissions scenarios.”

Some researchers from Princeton University drove that point home with a recent paper in the journal Nature Communications. Jun Yin and Amilcare Porporato’s paper, “Diurnal cloud cycle biases in climate models” details how they carefully analyzed satellite data from 1986 to 2005 and compared the information they gleaned to what the models produce.  The two determined how the time of day that clouds form in reality versus the time of day averaged in the models can affect the amount of solar radiation the models predict.

In the climate models, the cloud cover peaks in the morning. In reality, the cloud cover peaks in the afternoon – the same time the radiation coming from the sun peaks. The amount of clouds and types of clouds between the earth’s surface and the sun make a difference in how much energy from the sun we receive. The climate models’ were over-estimating that amount and potentially forecasting hotter and drier conditions based on it.

The paper states, “Thus, on the one hand, consistent biases in DCC [diurnal cycle of clouds] between present and future climates give rise to similar TOA [top of the atmosphere] reference irradiance, so that the model tuning made for current climate conditions still remains largely effective for the global mean temperature projections. On the other hand, consistent biases have the potential to increase the uncertainty of climate projections.” In simpler terms, the researchers don’t think the temperature forecasts are completely wrong, but they have shown the margin of error may be much greater than most scientists have acknowledged up to this point.

The hope is for the results of the study to be used to improve the current models.

In another story, the National Oceanic and Atmospheric Administration (NOAA), released the news on Tuesday that they are in the third phase of a massive supercomputer system upgrade. This year’s improvements increase the processing speed to 8.4 petaflops and 60 percent more storage capacity. The added speed and storage will allow for more initial conditions data – extremely important information for forecasting – and higher resolution, which will help with accuracy with respect to geographical space and time.

The goal is to improve our forecasting capability, especially when it comes to warning of dangerous storms. The forecasting model specifically mentioned in the press release is the Global Forecasting System (GFS), which has a reputation among many forecasters of often being less than accurate more than two or three days out, even though it produces predictions for 10 days out. Improvements to the GFS are needed and quite welcome!

If you’re not a meteorologist or climatologist, you likely don’t know the frustration of making a forecast based on science and technology – much more than we had fifty years ago – and still knowing that there is a chance the models we rely on are missing critical input and getting it wrong. While most people may not consider a few degrees error in temperature a horrible thing, they’d probably agree when the temperature happens to be around 32 degrees, a few degrees in either direction can make a big difference in our weather reality.