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.

Dramatic Soil Moisture Transformation over North Carolina Associated with Flooding Rainfall from Hurricane Florence

Dramatic Soil Moisture Transformation over North Carolina Associated with Flooding Rainfall from Hurricane Florence

As anticipated, Hurricane Florence resulting in monumental rainfall totals, particularly across southern and eastern North Carolina.  This past week’s rainfall totals are depicted in Figure 1, derived from the NOAA/National Weather Service Advanced Hydrologic Prediction Service (AHPS).  Widespread totals exceeded 10” across most of southern/eastern North Carolina and far eastern South Carolina, with maximum rainfall of more than 20” along and within a few counties of the Atlantic Coast.


Figure 1.  Weekly total rainfall (inches), valid 11-18 September 2018, from the National Weather Service Advanced Hydrologic Prediction Service (AHPS) product.  Four counties are denoted, for which soil moisture histogram animations are shown later in this article.

The extreme rainfall dramatically impacted the soil moisture, which underwent a substantial transformation from very dry to near-saturation across south-eastern North Carolina.  Figure 2 shows soil moisture retrievals before and after Hurricane Florence from NASA’s Soil Moisture Active Passive (SMAP) mission, which estimates near-surface soil moisture (~top 5 cm) in near-real-time derived from passive microwave satellite observations.  A 6-day animation of hourly SPoRT-LIS simulated 0-100 cm relative soil moisture images overlaid with Multi-Radar Multi-Sensor rainfall contours (Fig. 3) nicely shows how the predominantly very dry soils across North and South Carolina prior to Florence were quickly moistened to near saturation over just a few days.  [Ongoing research at SPoRT seeks to further improve the experimental soil moisture estimates by assimilating SMAP retrievals into the SPoRT-LIS framework.]


Figure 2.  NASA Soil Moisture Active Passive (SMAP) Level 2 soil moisture retrievals from before (top panel; valid 11 September) and after Hurricane Florence (bottom panel; valid 16 September).


Figure 3.  Animation of hourly SPoRT-LIS 0-100 cm relative soil moisture images overlaid with MRMS precipitation contours, valid for the period 0000 UTC 12 Sep to 2300 UTC 17 Sep 2018. [Click on image for full resolution]

Similar to that shown in a companion blog article, Figure 4 shows the evolution of shallow (0-10 cm) to total column/deep (0-200 cm) soil moisture percentiles relative to the SPoRT-LIS 1981-2013 climatological database.  Anomalously dry soil moisture is depicted by orange/red colors, while anomalously wet soil moisture is given by green/blue colors.  Prior to Hurricane Florence, much of South Carolina and southern parts of North Carolina were experiencing unusually dry soil moisture for this time of year.  Despite the capacity for the soils to receive moisture, the historic rainfall was enough to overcome soil moisture deficits, quickly leading to near-saturated soil conditions in all model depths, and ultimately substantial flooding.  An interesting feature to note after the storm impact is the very high soil moisture percentiles greater than 98th percentile across South Carolina in the shallow layers.  Meanwhile, the deeper soils experienced excessive soil moisture percentiles above the 98th percentile predominantly over North Carolina where the heaviest rainfall occurred and where the pre-storm dry anomalies were not as large as in South Carolina.

Fig4a_vsm0-10percent_20180910-17_nc_animFig4b_vsm0-40percent_20180910-17_nc_animFig4c_vsm0-100percent_20180910-17_nc_animFig4d_rsm02percent_20180910-17_nc_animFigure 4.  Daily animations of SPoRT-LIS soil moisture percentiles relative to 1981-2013 climatology, valid from 10 to 17 September over model depths at (top image) 0-10 cm, (2nd image) 0-40 cm, (3rd image) 0-100 cm, and (bottom image) total model column 0-200 cm.  [Click on each individual image for full resolution]


Finally, the dramatic transformation in soil moisture is nicely highlighted by examining the present-day, county-averaged values relative to the 1981-2013 climatological distributions, as shown in Figure 5 at four select counties in North Carolina.  Robeson and Cumberland counties (first and third images in Figure 5) experienced the driest soils prior to Hurricane Florence (westernmost counties in Fig. 1), whereas New Hanover and Craven counties (second and fourth images in Figure 5) were more moist prior to Florence’s rainfall.  Each of the four sampled counties ultimately experienced record daily soil moisture values by 17 September (99.9th percentiles).  However, the moist antecedent soils in Craven county led to record soil moisture values by 15 September, whereas the other counties that began with drier soils achieved record values by 16 or 17 September.  In summary, despite predominantly dry soils prior to Hurricane Florence across much of the Carolinas, the tremendous 10-30”+ rainfall totals led to a quick saturation of the soils and massive, widespread flooding.

Fig5a_Robeson_County_NC_7dayloop_ending_20180917Fig5b_New_Hanover_County_NC_7dayloop_ending_20180917Fig5c_Cumberland_County_NC_7dayloop_ending_20180917Fig5d_Craven_County_NC_7dayloop_ending_20180917Figure 5. Daily animations of SPoRT-LIS total column, county-averaged soil moisture histograms, valid from 10-17 September 2018 for (top image) Robeson county, NC [city of Lumberton], (2nd image) New Hanover county, NC [city of Wilmington], (3rd image) Cumberland county, NC [city of Fayetteville], and (bottom image) Craven county, NC [city of New Bern].  Gray bars represent frequency distribution of 1981-2013 climatological 0-200 cm relative soil moisture values, vertical colored lines are reference percentiles, and black dashed line is present-day, county-averaged soil moisture value. [Click on each image for full resolution]


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

Hurricane Florence to Impact the Carolinas with Massive Rainfall

All eyes are on North and South Carolina as Major Hurricane Florence approaches the region over the next two days.  One important component to the official forecast is for the storm to slow down as it approaches the coast, due to the collapse of major atmospheric steering currents.  As a result, the NCEP Weather Prediction Center is predicting extreme rainfall amounts, especially for southeastern coastal North Carolina where 15-20”+ of rainfall is anticipated over the next 7 days (Fig. 1).


Figure 1.  NCEP Weather Prediction Center 7-day rainfall forecast, valid for the period 1200 UTC 12 September through 1200 UTC 19 September 2018. [Click image for full view]

An examination of the antecedent soil moisture is helpful to qualitatively assess the ability of the ground to absorb some of the moisture from the incoming rainfall.  Figure 2 shows a collage of shallow to deep soil moisture percentiles from 12 September within the four layers of the Noah land surface model, as being run in real time within NASA SPoRT’s configuration of the Land Information System (i.e., “SPoRT-LIS”).  The percentiles are derived from a 1981-2013 database of SPoRT-LIS daily soil moisture values in order to compare the present-day soil moisture to historical values on any given day of the year.  In Fig. 2, we see that recent soil moisture values are historically quite dry over central/northern South Carolina and into far southern North Carolina, with values under the 10th percentile (and even 2nd percentile, yellow/red shades) in some areas.  Meanwhile, as one traverses inland and northward, the soils steadily moisten to anomalously wet conditions (green/blue shades), especially over interior North/South Carolina to the Appalachian Mountains.


Figure 2.  SPoRT-LIS soil moisture percentiles on 12 September 2018, relative to 1981-2013 daily climatological values for the following layers: (a) 0-10 cm (top model layer), (b) 0-40 cm (top two model layers), (c) 0-100 cm (top three model layers), and (d) 0-200 cm (all four model layers). [Click image for full view]

The dry soil moisture anomalies near the coast suggest that the soils will initially be able to absorb incoming rainfall fairly effectively.  However, as prolonged heavy rainfall continues with the expected slow movement of Hurricane Florence, the soils should quickly become saturated, thereby leading to enhanced runoff and flooding potential over time.  So while having dry soils will be of some help early in the event, a prolonged exceptional rainfall up to 20”+ will lead to substantial flooding regardless of the initial soil moisture distribution.

The blog author documented a similar scenario (also over South Carolina), where substantial moisture from Hurricane Joaquin in Autumn 2015 led to 20”+ rainfall totals, largely occurring over dry soils in an area of moderate to severe drought, thereby completely eliminating the drought classification in South Carolina and producing substantial flooding.  A similar scenario was also seen associated with Hurricane Harvey in southeastern Texas last year, where very dry soils were prevalent prior to Harvey’s landfall north and west of Houston Metro.  However, given the very prolonged exceptional rainfall event, incredible soil moistening and flooding occurred anyway in much of southeastern Texas.


Fog at Sunrise with RGBs using Visible Imagery

Fog at Sunrise with RGBs using Visible Imagery


Nighttime Microphysics RGB via GOES-16 at 1122 UTC, 13 August 2018 over the Southeast U.S.

During the early morning of 13 August 2018, clear skies resulted in wide spread low clouds and fog over the East/Southeast.  The image above is the Nighttime Microphysics (NtMicro) RGB via GOES-16 at 1122 UTC or 7:22 and 6:22 AM for Eastern and Central times respectively.  At this time the low clouds and fog in shades of cyan are still apparent, but soon this coloring will fade as solar reflectance at sunrise will influence the shortwave IR used in the RGB and therefore, the NtMicro will be rendered ineffective (see mp4 animation).  Typically, visible imagery is used at sunrise to continue to monitor fog in small-scale valleys, often with a lack of in situ observations.  The new capabilities of GOES-16 provide new RGBs for daytime use that include the 0.64 micron visible channel.  The Natural Color RGB, originally developed by EUMETSAT is available within AWIPS (as ‘Day Land Cloud’), and it uses the visible channel in it’s blue component.  Below is a slide show of the NtMicro, Natural Color and Visible RGBs just after sunrise (1222 UTC).  Note that the Natural Color RGB (also see mp4 animation) shows the fog and water clouds in gray while ice clouds are in cyan.  The Natural Color RGB can be used through the day to monitor the microphysics of cloud tops due to the use of the 1.61 micron channel, and it also provides qualitative land surface information via the 0.87 micron channel.   A legacy ‘Visible’ RGB (also see mp4 animation) that uses the visible in the red and green components (‘Day Land Convection’ within AWIPS), also provides value to monitor fog after sunrise as it depicts warm clouds in yellows and cold clouds in grays to white in daytime.

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GLM & Public Safety: An Important Caveat

As great as is to use data from the Geostationary Lightning Mapper, there is an important caveat forecasters have to consider when using the data.

During the afternoon of July 11, typical “air mass” showers and thunderstorms were developing across northern Alabama, including several south of the Huntsville International Airport.  At 12:18 PM CDT, the GLM Flash Extent Density data started to light up with these cells, including one larger flash at 12:21 PM.  (Huntsville airport is marked by the eastern concentric yellow circles.)

KHTX radar valid 1722 UTC 11 July 2018 and GLM FED valid 1721 UTC

As I’ve noted before, we issue airport weather warnings for Huntsville if lightning is within 5-10 miles, heading towards the terminal.  So forecasters were justifiably alarmed that GLM flashes were starting to show up within the 10-mile range ring, and just barely edging towards the 5-mile ring.

But here is where that caveat comes into play: the parallax effect.  Radar showed the actual echoes associated with these flashes to be well to the south of the GLM flashes.  Earth Networks Total Lightning data from the same time period showed lightning confined to these cells well outside the 10-mile range ring.  Furthermore, the cells were moving away from the field.

KHTX radar valid 1722 UTC 11 July 2018 and Earth Networks total lightning valid 1721 UTC

KHTX radar valid 1722 UTC 11 July 2018 and Earth Networks total lightning valid 1721 UTC

It’s worth noting that GLM showed a greater spatial extent during some of these flashes, but Earth Networks was much closer in location to the radar.

So while a cursory glance at the GLM data might lead to an airport weather warning, it was important to double-check GLM against the radar–and recognize that an AWW was not necessary in this case.  The same caution will need to apply as we begin applying GLM to other public safety situations.