High-resolution Satellite Imagery Assists in NWS Damage Surveys…

The NASA Earth Science Disasters Program and NASA SPoRT has been working with the National Weather Service and the Damage Assessment Toolkit (DAT) team for a number of years on the production of hi-resolution satellite imagery for inclusion in the DAT, and training on the usage of the imagery.  Imagery from the Landsat satellite at 30 m resolution is available regularly in the DAT as swaths become available, but higher resolution imagery from sources such as Sentinel (~10 m) and Worldview (<1 m) are available upon request through the USGS Hazards Data Distribution System.  These images are then processed by members of SPoRT and the Disasters Program for inclusion in the DAT.

Several tornado events earlier this year have helped to illustrate the effectiveness of the imagery for post-storm analysis and damage assessment.  This is especially true when damage is of sufficient magnitude and spatial extent to be resolved by the particular satellite instrument and cloud-free (or near cloud-free) conditions exist at the time of the satellite pass.  On April 12-13, a number of tornadoes developed and moved across the Southeast region of the U.S., some of which produced damage up to EF-4 scale in parts of southeastern Mississippi.  Meanwhile, closer to the Tennessee Valley, EF-2 tornado damage occurred in portions of the Huntsville and Birmingham, AL County Warning and Forecast Areas (CWFAs), with a tornado up to EF-3 strength damaging portions of Chattanooga in the Morristown, TN NWS CWFA.

This first image of damage is along a small portion of Interstate-65 in Cullman County, AL, and just south of the city of Cullman (Image 1 below).  Notice the area of damaged vegetation, which consisted of downed trees to the north of the preliminary tornado path and just east of Interstate 65 (red circle).  Although some of this area was shaded by clouds, damaged trees were clearly evident.  The location of damaged vegetation allowed for a relocation of the damage indicator (green triangle) farther north, which was originally closer to the green line.

Image 1.  Worldview imagery at approx. 1631 UTC 3 May 2020 of areas near tornado damage in Cullman County, AL.  The green line indicates the preliminary tornado track path, and the red circle indicates the swath of downed trees, while colored triangles represent the degree of tornado damage.

A zoomed-in look at the damage also shows that trees were not just downed or uprooted, but snapped.  Imagery from sources such as Worldview is of sufficiently high resolution to see snapped tree trunks, which will often be indicated by small, bright dots against the darker green/brown background, due to the higher reflectivity of wood on the inside of the tree.

Image 2.  Zoomed in from Image 1.  Some of the snapped trees in the image are annotated.  Snapped trees were also verified by visual ground inspection.

 

Farther to the east, along the same damage path in Cullman County,  a damage swath was previously undetected due to its distance from the road network that made visual ground inspection impossible (without a trek through the woods!).  However, the advantage of satellite imagery allowed for assessment of the tree damage (Image 3 below).  In Image 3, the blue triangle denotes the damage point that was added thanks to the presence of tree damage indicated by the satellite image.  Notice the lack of small bright dots as in Images 1 and 2 (in the red circle), suggesting that most if not all of these trees in this area were uprooted rather than snapped.

Image 3.  Worldview image at approx. 1631 UTC 3 May 2020.  The swath of downed trees can be clearly observed from SW to NE extending from near a small church and cemetery near the bottom of the image.  This damage could not be observed from the road seen in the bottom to left portions of the image.  The blue triangle (at the upper left part of image) indicates the tornado damage point added to the survey.

Another advantage of the DAT is the ability to underlay maps or other baseline imagery below the near real-time hi-res imagery.  Not only does this allow for proper geographic referencing, but the ability to assess vegetation type and state at a previous time.  Image 4 (below) is one such representative image in the DAT taken from the cold season.  Notice the lack of leaves and long shadows extending from the hardwood trees in the image.  However, the softwood trees, likely consisting of cedar and pine species, remain green.  Notice that the blue triangle was located within a patch of softwood trees, allowing for a better identification of tree types and thus the proper Damage Indicator to assess the wind rating for the uprooted trees.

Image 4.  Background image of the same area shown in Image 3 above.  Notice the area of damage (around the blue triangle) was within a patch of softwood trees.

Lastly for this post, we’ll take a look at another area of damage in Cullman County, just downstream and farther east along the same damage path.  This was also an area of previously unknown damage due to the lack of a specific report and the inability to view the damage from a nearby roadway.  The tree damage in this location was similar to the damage shown in images 1 and 2, with numerous bright dots indicating trees were likely snapped.

Image 5.  Worldview image at approx. 1631 UTC 3 May 2020.  A swath of tree damage can be extending southeastward just south of County Road 616 in east central Cullman County.  The green triangle indicates the added damage point due to the indication of damage in the satellite imagery.

The background reference image (Image 6 below, same imagery type as used in Image 4) from the same location however, showed that much of this scene was dominated by hardwood trees.

Image 6.  Background image of the same area shown in Image 5 above.  Notice the area of tree damage (around the green triangle) was within an area of mixed forest, but primarily consisted of hardwood trees.

This allowed for the proper Damage Indicator again to be used suggesting a higher wind speed rating than the damage from the previous scene (Image 3).  Due to the presence of many snapped hardwood trees, the damage here was rated EF1.

So, in these cases, we demonstrated several uses of the high-resolution imagery for damage assessment:  the ability to better affix damage points, locate damage otherwise hidden from typical roadway viewing, detect uprooted versus snapped trees, and identify primary tree types that were damaged in a location and assign proper damage indicators.

We would like to thank members of NASA MSFC, and the USGS who helped make this post possible, along with Digital Globe for permission to use and distribute the imagery in these examples.

-Kris and Lori

 

 

SPoRT Embarks on a Project to Produce Stream Height Forecasts using Machine Learning

The use of various soil moisture parameters from the SPoRT-LIS for monitoring drought conditions and assessing flood risk has been ongoing for years, and has been demonstrated for efficacy at other collaborative offices.  The use of soil moisture output for drought analysis is relatively straight-forward.  However, the use of the data for assessing flood risk has always been a bit more complicated and has involved the development of significant thresholds of rainfall and soil moisture that lead to flooding based on forecaster experience.  This method lends itself to some degree of subjectivity and has always been less quantitatively robust than preferred.  Nevertheless, the SPoRT-LIS data have provided valuable information regarding the state of soil moisture and the potential for flooding in near real-time.  There are older posts on the blog that describe the application of the data for flood risk assessment.

Now, the SPoRT group is embarking on a new project, employing a machine learning technique to provide a more quantitative measure of the relationship between soil moisture values, rainfall and stream height.  This new methodology involves the use of a Long Short-Term Memory (LSTM) Network.  The LSTM model for a particular drainage basin can be trained using a history of SPoRT-LIS soil moisture values, gauge height observations, and precipitation from the Multi-Radar Multi-Sensor Quantitative Precipitation Estimation data set.  Using this training data set, the forecast model for each basin is then run in real-time using the most recent gauge height observations, SPoRT-LIS soil moisture at various depths, and quantitative precipitation forecasts (QPF).  For this initial version of the gauge height forecasts, we’re using QPF values from the GFS model and the WPC.  One of the advantages of this type of modeling is that the great majority of computational power is on the front-end to train the model, while running the model in real-time is computationally much less expensive than running a hydrologic model.  Thus, we can run multiple precipitation scenarios for any basin quickly in real time.  For example, for the 40+ basins in our initial evaluation, the amount of processing time needed to run each basin at 6-hourly time steps out to 5 days with two different precipitation schemes takes just about 5 minutes!

The SPoRT group is working with some of our collaborative NWS offices (Huntsville, Nashville, Morristown, Sterling) and the Lower Mississippi and Mid-Atlantic River Forecast Centers (LMRFC and MARFC, respectively) for this initial test and evaluation of the stream height forecasts.  Although SPoRT has produced models for several thousand basins in the Southeast CONUS domain, this initial evaluation will involve a sub-set of streams, shown in the image below.

Image 1.  Gauge locations (black dots) for the initial evaluation of real-time gauge height forecasts from NASA SPoRT.

So, one might be asking…why is SPoRT engaged with stream height forecasting?  It’s important to remember that the SPoRT paradigm involves working closely with collaborative partners and assessing forecaster needs.  One of those needs involves having a better sense of flood risk at mid to long timescales during the 7-day forecast period.  Let us explain.  Operational gauge height forecasts from the RFC may not incorporate precipitation into the hydrologic models beyond one or two days due to operational and model limitations and constraints.  However, this can be problematic if an area is expecting heavy rainfall in the period beyond a day or two.  Take for example the current flooding event occurring across parts of the Tennessee Valley.  Operational gauge height forecasts for the Flint River (at Brownsboro, AL) from the afternoon of February 3rd indicated no rise forecast for the river (Image 2).  This is because the hydrologic models were not incorporating precipitation into the models as it was before the 48 hour cutoff.

Image 2.  Graph of gauge height observations (dotted blue line) and forecasts (dotted red line) for the Flint River at Brownsboro.  The forecast was valid approximately 1440 UTC 3 Feb 2020.  Observations are current through about 22 UTC 5 Feb 2020.  Horizontal bars at the top of the image indicate flooding thresholds (yellow=Action Stage, orange=Minor Flood, red=Moderate Flood)

However, heavy rain was expected in the region, which would certainly lead to some river rises.  The question is…how much?  Our old rules of thumb would have suggested flooding likely, based on soil moisture values and expected rainfall.  But again, the old rules didn’t indicate the time frame for flooding or the degree of flooding…it was generally just a qualitative “likely” or “not likely”.  So, to help alleviate this gap in knowledge, the new methodology provides objective, deterministic forecasts of stream or gauge height.  The image below shows the gauge height forecasts from the SPoRT LSTM models valid at about the same time.  Notice that the forecasts based on both GFS and WPC QPF scenarios indicated flooding was likely, while the higher precipitation from the GFS suggested flooding would reach Moderate Stage.  So, it’s easy to see here one of the advantages this type of modeling can have for overall hydrologic forecasting and situational awareness for the threat of flooding.

Figure 3.  SPoRT LSTM gauge height forecasts for the Brownsboro River at Brownsboro.  The black line shows observations, up to analysis time at 12Z 3 Feb 2020.  The blue dashed line contains gauge height forecasts based on GFS QPF, while the red line contains forecasts based on WPC QPF.  The blue vertical bars indicate 6-hourly GFS QPF, while the red bars indicate 6-hourly WPC QPF.

This post has become rather long.  So, we’re going to leave it here for now.  We’ll be providing more information about this project and discussing other advantages and limitations of our stream height forecasts in some upcoming posts as we continue this evaluation over the next couple of months.

– Kris and Andrew

Normalized Burn Ratio (NBR) Imagery in AWIPS…

Landscapes that have succumbed to wildfires, or burn scars, present especially difficult hydrologic forecasting challenges for National Weather Service (NWS) Offices since they can be conducive to the development of flash flooding and debris flows.  While the relationship between burn severity and this threat is rather complicated and dependent on a number of factors, determining the severity of the burned landscape can be important.  In order to assess this threat, professionals from a range of disciplines comprising Burned Area Emergency Response (BAER) Teams conduct intensive field surveys at the burn site.  BAER Teams conduct surveys as soon as team logistics and conditions allow, including containment levels of the wildfire (50% to 80% in many cases).  However, the threat for the development of debris flows and flash flooding can occur before these assessments can be made and as the wildfire is still actively burning.  Additionally, surveys are not conducted at all burn scars, especially in non federally-owned lands.  Traditionally, satellite imagery of burn scars has been used to help remedy this gap in knowledge about burn severity at any given location.  This imagery utilizes contrasting spectral properties between burned areas and healthy vegetation from a combination of Near-IR (~0.86 µm) and Shortwave IR (~2.25 µm) bands.  Imagery from Landsat and other high-resolution instruments has commonly been sought and used in associated analyses, but passes from high-resolution imagers can be infrequent, and cloud cover cover can obscure a single pass.  Thus, waiting periods for this type of imagery can be days to weeks depending on temporal availability of satellite passes and weather conditions.  To help with this issue, NASA SPoRT has developed the generation of NBR imagery in real-time in the Automated Weather Interactive Processing System (AWIPS) using data from the GOES-16 and GOES-17 satellites (Image 1).  Additionally, imagery from the VIIRS instrument aboard S-NPP has also been developed and transferred to AWIPS on an experimental basis.

Image 1. GOES-16 NBR imagery ((0.86 µm – 2.25 µm) / (0.86 µm + 2.25 µm)) overlaid with part transparent Visible (0.64 µm) imagery, 1751 UTC 8 Nov 2019

The compromise with GOES imagery is the lack of higher-resolution and thus detail observed in other imagery, yet analysis of a few fires so far this past fire season has indicated good agreement between GOES and VIIRS imagery.  A few examples are posted below.  Based on the color scale used, healthy/undisturbed vegetation is indicated by green colors, while burned areas appear in colors ranging from brighter yellows to oranges to reds.  The difference in resolution between the 0.86 and 2.25 µm bands in GOES-17 imagery causes “false” signatures along bodies of water.

Image 2. Woodbury Fire burn scar, GOES-17 NBR 1936 UTC 1 July 2019 (left), S-NPP NBR 1936 UTC 1 July 2019 (right), along with 2019 Fire Perimeters (black outlines)

Image 3. Kincade Fire burn scar, GOES-17 NBR 2101 UTC 7 Nov 2019 (left), S-NPP NBR 2057 UTC 7 Nov 2019 (right), along with 2019 Fire Perimeters (black outlines)

Image 4.  Recent So. California fire burn scars, GOES-17 NBR 2056 UTC 7 Nov 2019 (left), S-NPP NBR 2057 UTC 7 Nov 2019 (right), along with 2019 Fire Perimeters (black outlines)

While BAER Teams and Incident Meteorologists (IMETs) have also expressed a desire to have these types of imagery outside of AWIPS, in GIS-friendly formats, the advantage of making the imagery available in AWIPS is that forecasters can overlay it with other relevant hydrologic data sets that may help forecasters to better estimate the threat for flooding and debris flows.  Another advantage of having data generated from GOES is the high temporal resolution of the data, allowing near-continuous analysis of burn scar development as the fire is ongoing (provided clear sky conditions from clouds or smoke).

Image 5. Sample loop of the Woodbury Fire in AZ, GOES-17 NBR overlaid with partial transparent visible (0.64 µm) imagery, 2101-2251 UTC 17 June 2019.  Notice the burn scar that has already developed in western parts of the burn area (orange/yellow colors), while smoke can be seen emanating from the ongoing fire in NE parts of the fire complex (red colors).

While related development work is continuing, the SPoRT team will be discussing the potential use of this imagery with collaborative NWS offices, especially in the West CONUS,  for the next wildfire season.

-Kris W.

Geostationary Lightning Mapper (GLM) Data Used to Aid in Warning Decision…

The NWS office in Huntsville, AL (HUN) has had a long history with the use of total lightning data in operations, which stretches back to the office’s inception (after NWS modernization) in 2003.  Back then, and until its removal to South America for GOES validation testing, the HUN office largely used data from the North Alabama Lightning Mapping Array (NALMA). Lightning data sources from NLDN and ENTLN have also been used to varying degrees, but the advent of the GLM aboard GOES-16 brought a new era of lightning observations.  Because of the office’s participation in early operational testing of the GLM, its use and familiarity have gradually increased over the past year.  This was probably made easier due to our familiarity with total lightning data from the NALMA network.  Generally, GLM data have been used in much the same way as those from the NALMA network, especially with regards to situational awareness purposes (i.e., airport weather warnings, real-time weather watches for EM partners, initial cell electrification, etc.).  The use of the data to aid in severe weather warning decisions has been a bit slower to evolve, as might have been expected.  After all, there are differences in the way the NALMA observes lightning as compared to the GLM.  Values from the GLM have typically been “muted” compared to those from NALMA, so forecasters have had to make internal adjustments and recalibrate, if you will, what is considered significant.  However, the physical mechanisms that generate increases in total lightning, that is, increases in mixed-phase updraft volume, are essentially observed either way.  Thus, GLM data can still be useful to relate important information about storm/cell evolution, and can help to “tip the scales” in the balance of evidence about whether or not a warning may be needed.

This particular application of the GLM data occurred this morning with operational meteorologists at the HUN office.  The short image loop below shows thunderstorms moving across northwestern portions of Alabama between 1226 and 1300 UTC.  The top panel of the image contains data from the KGWX radar (0.5 degree reflectivity), while the bottom panel contains GLM 1-minute Flash Extent Density (FED) data.  Notice that lightning activity is relatively limited initially as the storm moves across western Franklin County, AL (near center of image), with 1-min FED values ranging between 2 and 6 flashes per minute.  Then, at the 12:37 UTC time mark, flashes begin an increase that manifests in a statistical lightning “jump” (GLM sigma > 2).  The warning meteorologist at the time was watching this cell for potential severe weather, and observed the sudden increase in FED values.  This, together with other radar and satellite observations (not shown here), suggested that a severe weather warning was necessary as wind signatures aloft gradually increased.  A warning was subsequently issued at 1245 UTC.  Incidentally, this thunderstorm did end up producing some wind damage, with trees reported down in south-central portions of Franklin County.  Notice also that a number of strong cell signals were detected by radar as indicated by the higher dBZ values across the domain.  Another use of the GLM is allowing meteorologists to focus on the cells with the strongest updrafts, making the overall radar interrogation and warning process more efficient.  This case can help to demonstrate that the GLM can be used as an important indicator of storm evolution and as a useful operational tool for the evaluation of severe weather potential.

-Kris W.

[Top] KGWX 0.5 deg Refl with NWS Severe Thunderstorm Warning (yellow box), [Bottom] GLM 1-min Flash Extent Density, 1226-1300 UTC 17 July 2019.

GLM “sees” apparent meteor flash in Western Cuba…

So, I was seeing some news reports on Twitter this afternoon about an apparent meteor that struck Western Cuba.  Pulling up data/imagery from the GLM in AWIPS, I was able to see some relatively high Flash Extent Density (FED) values from that area at the same time of the meteor report.  The first image below shows FED values (1818 UTC) overlaying GOES-16 Visible (0.64 µm) imagery at 1817 UTC.

Image 1. GLM data shows an apparent meteor flash over western portions of Cuba at ~1818 UTC 1 Feb 2019. The GLM Flash Extent Density overlay GOES-16 visible (0.64 um) imagery from ~1817 UTC.

Also, notice the large amount of lightning observed by the GLM in central portions of the Gulf of Mexico.  Here’s a short 30-min image loop around this time period (the suspected meteor flash shows up about midway through the loop).  Importantly, before the GLM sensor, the amount and extent of lightning activity over open ocean areas, away from ground networks, was generally not known, especially at such high spacial/temporal resolution.

Image 2. GLM (Flash Extent Density) and GOES-16 visible imagery (0.64 µm) loop from 1802-1830 UTC, 01 Feb 2019. An apparent meteor shows up in western Cuba at 1818 UTC in the loop. Also, notice the active deep convection and lightning over the Gulf of Mexico during the period.

 

 

SPoRT LIS Shows Dry Soils During High Plains Blowing Dust Event…

Yesterday while working on some Dust RGB related training materials, I was looking at the RGB in AWIPS and noticed a dust event unfolding in real-time in the central High Plains.  The loop below shows Dust RGB imagery, generated by GOES-East, yesterday, 28 Jan 2019 during the late morning and early afternoon hours.  The loop is centered over NE Colorado and SW Nebraska where you’ll see the blowing dust develop and spread southeastward.  In case you’re not too familiar with this type of imagery, the dust is represented by the magenta colors.  It’s also possible to observe some of the individual dust streaks or plumes within the larger blowing dust event, which help to show their locations of origin.  (By the way, sorry about the loss of image fidelity when saving from AWIPS to an animated GIF).

Image 1.  GOES-East Dust RGB imagery, approx. 1737-2002 UTC, 28 Jan 2019. The blowing dust is defined by the magenta colors, near the center of the imagery.

Research has shown that it takes the right combination of factors to loft dust particles sufficiently to generate these larger scale blowing dust events, partly based on soil moisture and winds.  The SPoRT LIS 0-10 cm volumetric soil moisture (VSM) analysis at 18 UTC indicated very low values in the blowing dust source region, with VSM percentages generally around 12-16% (Image 2).  The METAR observations also indicate sustained winds were 35-40 knots with stronger gusts over 40 knots at one locations in the area.

Image 2. SPoRT LIS volumetric soil moisture (background colors) overlaid with surface METAR plots (yellow figures), valid at 18 UTC, 28 Jan 2019.

This last image is a snapshot of the Dust RGB taken at 1902 UTC, overlaid with surface visibility and ceiling observations.  Notice that at station KHEQ in far northeastern Colorado, a ceiling of 100 ft and visibility of 7 SM was reported, which was likely due to the blowing dust.

Image 3. GOES-East Dust RGB and ceiling and visibility observations from ground observation stations at approximately 19 UTC, 28 Jan 2019.

Some SPoRT collaborative NWS offices in the West CONUS have utilized LIS VSM values to locate areas where the probability of blowing dust events is heightened under the proper conditions.  However, SPoRT is looking into opportunities to better predict where these events will occur.

SPoRT LIS Shows Low Soil Moisture Conditions Near Large N. Cal Fire…

Just making a quick post here as I noticed there were relatively dry soil moisture conditions at the site of a rather large fire that developed quickly in Butte County, CA today.  The first image is the Fire Temperature RGB from the GOES-16 satellite (mesoscale domain sector 1) at 2018 UTC, today, 8 Nov 2018.  In this RGB, the fire can be observed by  colors ranging from near red to near white, just east of Chico, CA.  Notice there are a few white pixels, indicating relatively high emissions from shorter wavelengths (1.61 µm), and thus, relatively hot fire temperatures.

Meanwhile, soil moisture data from SPoRT’s Land Information System show low soil moisture percentiles (from the 33-year climatology, next image below) at the fire’s location east of Chico.  In fact, these values are  below the 2nd percentile at the fire’s location.

Lastly, the one year change in deep layer soil moisture values (0-200 cm) also show significant decreases in soil moisture centered at the fire’s location and especially just east over the last year.

SPoRT is conducting research and working closely with members of the wildfire community in the western U.S. to transfer these and other data sets for operational decision-makers.

-Kris W.

3D GOES-16/17 Imagery at NWS Huntsville

Yes, you read the title correctly.  We have 3D visible imagery from the GOES-16/17 satellites in the Advanced Weather Interactive Processing System (AWIPS) at the Weather Forecast Office in Huntsville!  So, how did we do this?  I’ll explain.

Several days ago, Kevin McGrath at NASA SPoRT created Facebook and Twitter posts detailing the capability of generating 3D imagery when using both the GOES-16 and GOES-17 satellites in one image.  This is made possible by taking advantage of the slightly different viewing angles by the two satellites in their current GOES East and Center positions.  Yesterday, we explored the possibility of doing this in AWIPS here at the Huntsville WFO and were successful.  I’ll tell you how we did it (which is actually not that difficult), but first I’ll show some 3D imagery from around the Southeast U.S. region this morning.  By the way, to view the imagery in its full 3D glory, you’ll need some standard red/cyan 3D glasses.  Let us also add that the original imagery appears much better in AWIPS as there is always some loss of fidelity when generating images in .gifs and then transferring and viewing these from other platforms.  Anyway, hopefully you’ll get a good sense of the 3D aspects contained within the image loops, and I’ll add it’s better to view with your screen brightness turned up and under darker ambient conditions.

Image 1. GOES-16/17 3D Visible image loop (0.64 µm), 1307-1442 UTC, 14 Sep 2018

Next, we’ll take a closer look at some of these cloud scenes.  First, here’s a look at Hurricane Florence as it churns along the N. Carolina coast.  You may notice (as we did) that it is much easier to observe the differential motion and distinguish among the various cloud layers in this type of imagery.  Unfortunately, some of the image fidelity is lost when saving as a .gif, as observed particularly in the cirrus cloud layer in the image loop.

Image 2. GOES-16/17 3D visible image loop (0.64 µm) of Hurricane Florence, 1317-1452 UTC, 14 Sep 2018

It is rather extraordinary to view developing convection in 3D.  This convective cloud scene in the NW Gulf of Mexico details this capability well (Image 3).

Image 3. GOES-16/17 3D visible image loop (0.64 µm) centered over the NW Gulf of Mexico, 1317-1452 UTC, 14 Sep 2018

This next cloud scene is not as active, however, it is interesting how one can get a sense of the differences in cloud depth between the fog hugging some of the southern Appalachian valleys and the outer cirrus band extending far west of Hurricane Florence.

Image 4. GOES-16/17 3D visible image loop (0.64 µm) centered over the Southern Appalachian region, 1222-1357 UTC, 14 Sep 2018

Lastly, to demonstrate the advantage of this type of imagery, we thought we’d show a simple GOES-16 visible loop (Image 5) compared to a 3D visible loop (Image 6).

Image 5.  GOES-16 Visible image loop (0.64 µm) centered over south TX, 1447-1627 UTC 14 Sep 2018

Image 6.  GOES-16/17 3D visible image loop (0.64 µm) centered over south TX,

Now, you may notice a lack of “brightness” in the 3D imagery, which is due to the layering process.  But, perhaps you can get a better sense of the complex layered cloud scene over southern portions of TX in the 3D loop as we did.  Of course, as stated previously, there’s generally something lost in translation when moving and viewing graphics between various screens and viewing platforms.

So, now to answer the question…how did we do this?  Well, it was somewhat simple actually.  As you can see in the images, the GOES-17 image is layered on top and GOES-16 on the bottom.  Now, it doesn’t actually matter which satellite image is layered on top.  But, whichever one that is, it will need to be set to 50% transparency.  Then, we modified the color map in AWIPS, applying a pure black to red color curve for the GOES-17 reflectivity values, and black to cyan (or equal contributions of blue and green) for GOES-16.  When doing this initially, we used a simple linear stretch to the color map.  However, we realized a more appropriate methodology utilizes the default non-linear ABI VIS gray scale color map.  So, we simply modified that color map by changing all of the blue and green color values to 0.0, saving this as a new color map and applying this to GOES-17 imagery.  Taking the original color map again, we changed all of the red color values to 0.0 for the GOES-16 imagery.  Voila!  When viewing through the standard red (left eye), cyan (right eye) 3D colored glasses, the left and right eye will see the two GOES images from their respective viewing angles and the imagery appears in 3D.

The lingering question may be…so this is cool and all, but what is the application?  As suggested, this type of imagery does offer a more realistic depiction of the atmosphere and helps to differentiate different cloud layers.  Sure, there are some fantastic RGBs now that can aid in this too.  But, this is another tool in the forecaster toolbox, so to speak.  Additionally, I noticed yesterday and today that it is easier to get a sense of shear in tilted convective updrafts, and when speaking with forecasters at the WFO here, it helps provide them a more thorough and realistic conceptual model of the troposphere.  So, these are some things to consider.  We’ll be exploring more use of this imagery over the coming days/weeks.  The are some caveats to all of this.  First, people with significant red/green color deficiencies may not be able to view the 3D imagery as intended.  Second…we don’t know if this will still work once GOES-17 gets shifted to its eventual GOES-West position later this year.  There may be too great of a difference in the viewing angles.  A quick inspection of GOES-15/16 imagery using this same format seemed to indicate an issue there.  We’ll see.  Anyway, for now, this is a fascinating way to view the visible cloud scene.

-Kris White & Kevin McGrath

Passive Microwave Views of Hurricane Florence…

As Hurricane Florence has developed and flourished in the warm waters of the central and western North Atlantic, the NHC has been using data from microwave sensors aboard polar-orbiting satellites to obtain information about important physical characteristics of the hurricane not otherwise observed by conventional imagery from geostationary satellites.  Not only does the microwave data provide important information about the location, intensity and extent of precipitation bands and deep convection within the hurricane, but can also provide better fixes for the storm center location.  The first image below (Image 1) shows a GOES-16 visible image (~0.64 µm) at approximately 1812 UTC 12 Sep 2018.

Image 1.  GOES-16 Visible Image (~0.64 µm), 1812 UTC 12 Sep 2018

The visible image can be used to ascertain information about some physical characteristics of the hurricane, but the broad canopy of cirrus over much of the hurricane can obscure important, relevant features about banding structures, in particular.  Image 2 shows microwave data (~89 GHz) derived from the AMSR2 sensor at about the same time as the visible image (in Image 1).  Notice that much of the intense banding observed in the microwave data was concentrated along the W to N portions of the hurricane at this time, which might not have been immediately obvious based on the visible imagery alone.  In fact, notice the fairly thin band of convection along the SE side of the eyewall at 1812 UTC.

Image 2. 89 GHz (Horizontal) image from AMSR2, 1812 UTC 12 Sep 2018

Even an inspection of color-enhanced LW IR data/imagery (~10.34 µm) might have suggested a fairly even distribution of deep convection around the eyewall at this time (Image 3).

Image 3.  GOES-16 LW IR image (~10.34 µm), 1812 UTC 12 Sep 2018

However, the 10.34 um will observe cold cirrus cloud tops where present, which may have resulted from earlier convection, and ice crystals that have since been distributed more evenly around the upper-level outflow and not necessarily from recent convection.

Lastly, I thought I’d finish quickly with a loop of the available polar-orbiting passive microwave imagery over Hurricane Florence since early yesterday.  The background color that appears mostly static through the loop is the sea surface temperature data derived from the VIIRS instrument, which is produced by NASA SPoRT and sent to collaborative NWS offices through AWIPS.  Notice the abundance of orange/red colors in the basin through which the hurricane is moving, which is indicative of water temperatures in the mid 80s F (scale not shown).

Image 4. Available polar microwave imagery/data passes over Hurricane Florence since early Sep 11th, background data is sea-surface temperatures derived from the VIIRS instrument

Wet streaks in soil moisture observed in west Texas…

This morning I observed some rather odd looking streaks in the 10.3 µm imagery in western portions of Texas.  Sampling the 10.3 µm data revealed alternating areas of relative warm/cool skin surface temperatures in the cloud free conditions in the area on the upstream side of departing deep convection.  The temperature difference in the skin temperatures were around 5 C at this time.  The 10.3 µm image below was taken from appox. 1357 UTC this morning (3 May 2018).

Image 1.  10.3 um imagery from GOES-16, 1357 UTC 3 May 2018

Arguably, the streaks of lower temperature values showed up better in the 3.9 µm imagery.  Notice the streaks or alternating bands of yellow/orange colors in portions of west Texas.

Image 2.  3.9 um imagery from GOES-16, 1357 UTC 3 May 2018

Realizing these temperature differences were likely due to the recent convective rainfall, I looked up the SPoRT LIS 0-10 cm volumetric soil moisture data, which corresponded nearly perfectly with the streaks of relative lower temperature values (Image 3).  So indeed, this was due to the recent heavy, convective rainfall across the area.

Image 3.  SPoRT LIS 0-10 cm Volumetric Soil Moisture, 15 UTC 3 May 2018