Comparison of Soil Moisture Response in Hurricanes Harvey and Irma

Comparison of Soil Moisture Response in Hurricanes Harvey and Irma

After a record [nearly] 12 years between landfalling major hurricanes [cat 3 or higher], the United States has now experienced two major hurricanes making landfall less than three weeks apart from one another.  Hurricane Harvey brought exceptional record rainfall to southeastern Texas and southwestern Louisiana because it stalled shortly after landfall due to a lack of atmospheric steering currents.  Less than 3 weeks later, Major Hurricane Irma made landfall twice in Florida: once in the Lower Keys and again near Marco Island on the southwestern coast.  A long-lived cat 5 hurricane prior to landfall, Irma had a very large wind field which resulted in far-reaching impacts along the Florida East Coast, up to Charleston, SC, and inland to Atlanta, GA, with millions of households and businesses without electricity and/or water.

Here at the NASA SPoRT Center, we have been closely monitoring these two hurricanes through numerous social media and blog posts of unique satellite products and through SPoRT’s real-time instance of the NASA Land Information System (“SPoRT-LIS”).  This blog post serves to compare the soil moisture responses to hurricanes Irma and Harvey rainfall, as depicted by the real-time SPoRT-LIS output.  The Relative Soil Moisture (RSM) variable is shown throughout this article, since it takes into account the variations in soil composition by scaling the moisture availability between the wilting point (plants cannot uptake moisture) and saturation point (soil cannot hold any more water).  The SPoRT-LIS runs the Noah land surface model, which estimates soil moisture through 4 layers: 0-10, 10-40, 40-100, and 100-200 cm depth.  We first examine the response during Irma in the top 0-10 cm layer, followed by 0-100 cm layer for both storms, and then compare the total column (0-200 cm) values relative to historical values from a climatological database spanning 1981-2013 (33 years).

Figure 1 compares the weekly rainfall accumulation primarily from Hurricane Irma over the Southeastern U.S. to the August monthly rainfall total over Texas/Louisiana, primarily contributed from Hurricane Harvey during the final week of August. Rainfall from Irma was quite substantial in the Florida peninsula up to coastal South Carolina, where numerous locations measured over 10″ of rain in less than 2 days. Rainfall of 3-5″ extended inland to northern Georgia and central South Carolina, with lesser amounts generally below 3″ across eastern and northern Alabama (Fig 1, left panel).  The highest totals were along the southwestern and eastern Florida coasts.  This rainfall still pales in comparison to the widespread 20″+ that fell across a huge part of southeastern Texas and western Louisiana, albeit over a 5-6 day span.  Highest totals exceeded 50″ near Beaumont/Port Arthur, TX!

PrecipComparison

Fig 1.  Comparison of weekly rainfall estimate associated with Hurricane Irma (left), and August monthly rainfall estimate associated with Hurricane Harvey (right).

The 0-10 cm RSM animation in Fig 2 for hurricane Irma shows how quickly the top soil layer responds to incoming rainfall within the Noah land surface model in SPoRT-LIS.  The heavy rainfall rates up to 4″ per hour or more led to a quick saturation during 10 September across the Florida peninsula, eventually extending up to coastal Georgia and South Carolina on the 11th.  Similarly, as rainfall ends we can see the 0-10 cm RSM quickly decrease from south to north as the moisture infiltrates into deeper model layers and/or evaporates back to the atmosphere.  We also see that the top soil layer does not completely saturate across interior Georgia and Alabama, likely due to lower rain rates, drier initial soils, and different soil composition compared to the fast-responding sandy soils across Florida.

rsoim0-10_hurricaneIrma_10-12Sep_anim

Fig 2.  Hourly animation of SPoRT-LIS 0-10 cm relative soil moisture (RSM) and Multi Radar Multi Sensor (MRMS) quantitative precipitation estimates (QPE) from 0000 UTC 10 September through 1200 UTC 12 September 2017, associated with Hurricane Irma.

Meanwhile, the RSM averaged over the top 3 layers (0-100 cm; Fig 3) takes a longer time to moisten up during the heavy rainfall of Irma. We do see values approaching saturation across southwestern, central, and particularly northeastern Florida near the end of the rainfall event as the deeper soils have had an opportunity to recharge.

Over southeastern Texas and Louisiana (Fig 4), the 0-100 cm RSM animation shows how the prolonged, training heavy rainfall led to near saturation of the top meter of the Noah model, despite dry antecedent conditions (especially west of the Houston metro, where the RSM transitioned from less than 10% to nearly saturation!).  The much longer rainfall duration with hurricane Harvey led to sustained higher values of soil moisture in the top one meter.

rsoim0-100_hurricaneIrma_10-12Sep_anim

Fig 3.  Hourly animation of SPoRT-LIS 0-100 cm RSM and MRMS QPE from 10-12 September 2017, associated with Hurricane Irma.

rsoim0-100_hurricaneHarvey_25-30aug_anim

Fig 4.  Hourly animation of SPoRT-LIS 0-100 cm RSM and MRMS QPE from 25-30 August 2017, associated with Hurricane Harvey.

Finally, the total column 0-200 cm layer can require months or years to respond to rainfall events (or lack thereof), depending on the soil composition.  However, with major rainfall events like hurricanes Harvey and Irma, the total column RSM does respond dramatically and subsequently can depict substantial wet anomalies.  To that end, the SPoRT-LIS has a daily, county-based climatological database of modeled soil moisture from 1981-2013 from which current conditions can be compared to depict anomalies via percentiles relative to the 33-year distribution.  Fig 5 shows these percentiles color-coded to depict dry anomalies (less then 30th percentile) or wet anomalies (greater than 70th percentile) according to the scales beneath the figure.

Following hurricane Irma, we see that portions of southwestern and northeastern Florida have 0-200 cm RSM greater than the 98th percentile, as well as parts of west-central Georgia (Fig 5; left panel).  In general, the extreme wet percentiles are fairly spotty across the domain.  However, following hurricane Harvey (Fig 5; right panel), the 0-200 cm RSM percentiles are “off the charts” high, with dozens of counties experiencing soil moisture exceeding the [33-year] historical 98th percentile.  In fact, the soil moisture was SO anomalously moist following hurricane Harvey that the average daily value across all of Jefferson County, TX (Beaumont/Port Arthur) exceeded all values in the entire 33-year database by the end of August!  This unusual condition is highlighted in Fig 6, which shows a daily animation of historical 0-200 cm RSM histograms for Jefferson County, TX, with the current 2017 county-averaged values in the vertical dashed line.  We see that by the end of hurricane Harvey, the vertical dashed line is well above any values from the 33-year historical distribution, thereby quantifying how exceptionally unusual this rainfall event was in southeastern Texas.

PercentileComparison

Fig 5.  SPoRT-LIS 0-200 cm RSM percentile, valid at 1200 UTC on 12 September 2017 (post-Irma; left), and 30 August 2017 (post-Harvey; right).

Jefferson_County_TX_30day_realtimeLoop

Fig 6. Animation of daily distributions of 0-200 cm RSM for all SPoRT-LIS grid points residing in Jefferson County, TX (Beaumont/Port Arthur) during the month of August 2017.  Gray bars are the frequencies of 0-200 cm RSM from the 33-year SPoRT-LIS climatology; colored vertical lines are reference percentiles according to the legend in the upper right; and the bold vertical dashed line is the county-averaged value for the present day in August 2017.

Soil Moisture Conditions over Southeast Texas Prior to Hurricane Harvey

Soil Moisture Conditions over Southeast Texas Prior to Hurricane Harvey

As much-anticipated Hurricane Harvey approaches the southern and eastern coast of Texas today, it is worth examining the pre-existing soil moisture over the region to understand the capacity of the land surface to absorb the upcoming rainfall.  Granted, the amount of rainfall simulated by numerical guidance is off-the-charts high (e.g., today’s 0600 UTC initialized NAM model [Fig. 1] shows 84-hour maximum accumulated rainfall of over 60″ between Corpus Christie and Houston!!).  Thus, extreme flooding is anticipated, regardless of the amount that can be absorbed by the soils.

Fig1_NAMFLT_prec_precacc_084

Figure 1.  The NCEP/NAM model 84-hour forecast of total accumulated precipitation (inches) over Southeastern Texas, from the simulation initialized at 0600 UTC 25 August 2017 [image courtesy of College of DuPage forecast page].

SPoRT manages a real-time simulation of the NASA Land Information System (hereafter, “SPoRT-LIS“), running over the Continental U.S. at ~3-km grid resolution.  The SPoRT-LIS product is a Noah land surface model climatological and real-time simulation over 4 model soil layers (0-10, 10-40, 40-100, and 100-200 cm).  The climatological simulation spans 1981-2013 and forms the basis for daily-updated total-column soil moisture percentiles (forthcoming in Fig. 3), in order to place current soil moisture values into historical context.  For real-time output, the Noah simulation is regularly updated four times per day as an extension of the long-term climatology simulation.  It includes NOAA/NESDIS daily global VIIRS Green Vegetation Fraction data, and the real-time SPoRT-LIS component also incorporates quantitative precipitation estimates (QPE) from the Multi-Radar Multi-Sensor (MRMS) gauge-corrected radar product.  The climatological SPoRT-LIS is based exclusively on atmospheric analysis input from the NOAA/NASA North American Land Data Assimilation System – version 2.

Relative Soil Moisture output from the SPoRT-LIS over the 0-100 cm layer is shown in Fig. 2 over Southeastern Texas and Louisiana at 1200 UTC this morning.  A marked gradient between very dry soils to the west and moist soils to the east occurs in the vicinity of the greater Houston metropolitan area.  The soils in the region bounded by Corpus Christi, San Antonio, Austin, and Houston (areas forecast to have the greatest rainfall from Hurricane Harvey) are extremely dry prior to Harvey’s landfall.  This dryness will help to some extent in absorbing the initial rainfall from Hurricane Harvey.  But with such excessive rainfall being forecast over a prolonged time period (3-5+ days), it won’t be long before the upper portions of the soil column saturates and widespread areal flooding occurs.  In addition, the high forecast rainfall rates could easily result in flash flooding (despite prevailing soil dryness), especially further inland where terrain plays a more important role in runoff and flash flooding.

The total column relative soil moisture percentile from 24 August shows that historically-speaking, the soil moisture is slightly drier than normal, particularly along the coastal plain between Corpus Christi and Houston (Fig. 3).  In this corridor, the soil moisture is generally between the 10th and 30th percentile compared to the 1981-2013 climatological distribution for 24 August.

Fig2_rsoim0-100_20170825_12z_tx_cityLabels

Figure 2.  SPoRT-LIS relative soil moisture (RSM) distribution in the 0-1 meter layer across Southeastern Texas and Louisiana, valid 1200 UTC 25 August 2017.  RSM values of 0% represent wilting (vegetation cannot extract moisture from soil) and 100% represents saturation (subsequent rainfall becomes runoff).

Fig3_rsm02percent_20170824_12z_tx

Figure 3.  Total column (0-2 m) relative soil moisture percentile valid 24 Aug 2017, as compared to all 24 August soil moisture values from a 33-year climatological simulation of the SPoRT-LIS.

Finally, an hourly animation of the 1-day changes in 0-10 cm (top model layer) relative soil moisture show that the near-surface soils are quickly moistening between Corpus Christi and Houston, as the initial rainbands of Hurricane Harvey began impacting the coastal plain this morning.  As the soils continue to moisten rapidly from the top-down, subsequent rainfall will quickly lead to runoff and flooding.

Fig4_rsoim0-10diff1_20170825_anim

Figure 4.  Hourly animation of 1-day change in top-layer (0-10 cm) relative soil moisture, for the time period spanning 0000-1400 UTC 25 August 2017.  Each hourly image is a simple difference in 0-10 cm relative soil moisture between the current and previous day at the same valid hour.  Line contours depict one-hour QPE from the MRMS product, as input to the real-time SPoRT-LIS.

Stark contrast in Eastern U.S. soil moisture following Hurricane Matthew

Stark contrast in Eastern U.S. soil moisture following Hurricane Matthew

Major Hurricane Matthew left a trail of destruction in its wake from the Caribbean up through the U.S. East Coast.  As Hurricane Matthew tracked northward along a large portion of the U.S. Southeast Coast from Florida to North Carolina, the rainfall impacts worsened.  Figure 1 shows the weekly rainfall spanning 4-11 October, ranging from ~2-8 inches along the Florida East Coast to 10-20 inches in the eastern Carolinas.  Since antecedent soil moisture was highest in the eastern Carolinas (Fig. 2), the extreme rainfall led to the most serious flooding in this area.

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Fig. 1.  Weekly rainfall totals from 4 – 11 October 2016.

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Fig. 2.  Total Column (0-2 m) relative soil moisture prior to Hurricane Matthew’s impact on North and South Carolina, valid at 0000 UTC 7 October 2016.

Referring back to the precipitation totals in Fig. 1, we can see that there was a sharp rainfall gradient on the northwestern edge in the Middle Atlantic region.  Interestingly, this gradient in Hurricane Matthew’s rainfall coincided with a pre-existing transition zone between wet conditions near the Atlantic coast and drought conditions further inland from the Appalachians through New England.  The net result was to accentuate the wet-dry contrast already in place.  The animation in Fig. 3 highlights this contrast nicely by presenting the SPoRT-LIS daily total-column relative soil moisture percentiles from 1-12 October.  The percentiles are based off a 1981-2013 daily soil moisture climatology that SPoRT produced from its ~3-km resolution SPoRT-LIS simulation.  By 9 October, notice the incredible transition from excessively wet soil moisture exceeding the 98th percentile (Carolinas through the southern half of Delaware) to extremely dry soil moisture less than the 5th percentile across Pennsylvania into the Northeast (as well as much of the inland Southeastern U.S.).  In fact, total column soil moisture values are less than the 2nd percentile over a large part of Ohio, Pennsylvania, New York, and the New England states, indicative of the ongoing severe drought there.

fig3_loop

Fig. 3. Daily animation of SPoRT-LIS total column relative soil moisture percentile from 1 to 12 October 2016.

Post Frontal Orographic Snowfall Impacts NM

A slow-moving upper level storm system tracked east across northern NM and southern CO on 14-15 December 2015. A weak tap of subtropical moisture ahead of this system provided light to moderate snowfall mainly along the Continental Divide of western NM and the higher terrain running north-south through central and northern NM. Snow accumulations of 3 to 8 inches were reported ahead of and immediately behind the surface front and the mid level trough passage. A classic westerly,upslope flow event developed behind the upper wave as moist, unstable flow interacted with the north-south oriented higher terrain. Winter weather advisories and winter storm warnings were in effect over much of northern NM for the expectation of storm total snowfall of 8 to 12″ with locally higher amounts. Figure 1 depicts the distribution of advisories and warnings over northern NM on the Albuquerque National Weather Service public page.

Advisory and warning map for the ABQ CWA valid 15 December 2015.

Figure 1. Advisory and warning map for the ABQ County Warning Area valid 15 December 2015.

Poor radar coverage over northern and western NM makes it a challenge for assessing winter precipitation patterns and snowfall rates. Figure 2 shows a radar mosaic valid 1800 UTC 15 December 2015 utilizing an enhanced color curve to identify areas of lighter snowfall. Automated surface observations are sparse in this area however there are at least a few observations reporting snow where nothing is present in the radar reflectivity. Webcams at ski resorts serve as an excellent near real-time proxy for visualizing active snow accumulations in these poor radar coverage regions. Additionally, once daily snow accumulation reports from ski resorts aid the verification process following the winter event.

Figure 2. Winter radar mosaic from KABX valid 1800 UTC 15 December 2015. Note the orange circle depicting a large area of poor radar coverage.

The integration of satellite data allows forecasters to supplement these data void areas. The most recent interation of the NESDIS snowfall rate products available at WFO Albuquerque illustrate the snowfall rate derived from radar (Figure 3a) and the snowfall rate available from merging the POES satellite data with the radar data (Figure 3b). Note the grey areas overlaid on the map in Figure 3a indicate areas of reliable radar coverage. The snowfall rate derived from satellite data in Figure 3b clearly shows coverage outside of the area with reliable radar coverage. A very cold and unstable airmass in association with this precipitation suggested snowfall rates in the higher terrain would average between 20-30:1. The 18:1 image in the lower right of Figure 3b indicated rates around 0.4/hr.

FIgure 3a. Radar derived snowfall rate product over northern NM valid 1750 UTC 15 December 2015.

Figure 3a. Radar derived snowfall rate product over northern NM valid 1750 UTC 15 December 2015. Note the grey areas overlaid on the map indicating where reliable radar coverage exists. Upper left (liquid equivalent), upper right (10:1), lower right (18:1), lower left (36:1).

NESDIS snowfall rate product filling in the radar gaps over northern NM valid 1750 UTC 15 December 2015. Note the circles in the upper left image are the location of the webcams in Figure 4.

Although there is sparse coverage of automated surface observations around the higher terrain, webcams from ski resorts can verify the existence of moderate to heavy snowfall. Visibilities in the webcams below suggest snowfall rates higher than those depicted in the NESDIS products – visually, rates look closer to perhaps 1″/hr in the upper right and lower right images (Figure 4). One of our goals of this assessment is to combine information from the webcams with the more quantitative snowfall rate product to better estimate snowfall in data void areas. Snowfall reports from the Chama Railyard indicated 8.5″, Taos Ski Valley 6″, Ski Santa Fe 12″, and Pajarito Mountain 10″.

 

Figure 4. Webcams from across northern NM. Top left (Chama Railyard, yellow circle), Top right (Taos Ski Valley, white circle), Bottom right (Ski Santa Fe, red circle), Bottom left (Pajarito Mt, orange circle).

 

From drought to flooding in less than a week over the Carolinas, as depicted by SPoRT-LIS

A closed upper low over the Southeastern U.S. combined with a deep tropical moisture connection with Hurricane Joaquin led to historic rainfall and flooding over North and especially South Carolina over the weekend.  A wide swath of central South Carolina from the coast to Columbia received over 20 inches of rainfall in the past week (Fig. 1), much of it in the last 2-3 days.  Figure 2 shows the NASA Integrated Multi-satellitE Retrievals for GPM (IMERG) 24-h rainfall estimates displayed in AWIPS II compared to the official NWS/River Forecast Center rainfall estimate for the period ending 1200 UTC on 4 October.  This is erasing the prevailing drought in the Carolinas, which still had moderate to severe drought in the most recent U.S. Drought Monitor weekly product valid 29 September (Fig. 3).

SPoRT’s real-time configuration of the NASA Land Information System (SPoRT-LIS) runs the Noah land surface model to generate a “best modeled” soil moisture estimate at ~3-km resolution for enhanced situational awareness and input to local/regional numerical weather prediction models.  The SPoRT-LIS was assessed during summer/fall 2014 by the NOAA/NWS weather forecast offices (WFOs) at Houston, TX, Raleigh, NC, and Huntsville, AL. Following an expansion to a full Continental U.S. domain, the SPoRT-LIS was also evaluated more informally by the NWS WFOs at Tucson, AZ and Albuquerque, NM this past summer.  The primary areas of utility has been in drought monitoring and assessing areal and river flooding potential.  However, the Southwestern U.S. offices applied SPoRT-LIS soil moisture fields to enhance situational awareness for wildfire and blowing dust situations as well. Overall, a majority of the users during these assessments expressed substantial utility of the product due to a lack of other real-time high-resolution soil moisture products, and were confident enough to use the product as part of operational, public forecasts.

This extreme rainfall event in the Carolinas was captured nicely by the real-time SPoRT-LIS, which depicted some of the most dramatic changes in total column soil moisture ever documented by SPoRT collaborators.  Figure 4 compares the SPoRT-LIS 0-2 m total column relative soil moisture (RSM) from 28 September (left panel) and 5 October (right panel), with Figure 5 highlighting the 1-week change in 0-2 m RSM as displayed in AWIPS II.  The RSM represents how the volumetric soil moisture scales between wilting (0%) and saturation (100%) for a given soil composition, where the wilting point indicates that vegetation can no longer extract moisture from the soil and saturation indicates no more infiltration is possible (thus all new precipitation goes to runoff). Previous experience by the Huntsville WFO found that total column RSM values of ~60% and above tend to indicate an enhanced threat for areal and river flooding over northern Alabama; however, these thresholds can vary depending on river basin properties and regional soil composition.

Values of total column RSM typically ranged from ~25-35% on 28 September, prior to the significant rain event.  However, by 5 October, total column RSM increased to well above 65% in most areas of central South Carolina, and parts of southern and far western North Carolina.  The maximum weekly change in 0-2 m RSM (Fig. 5) exceeds 58% in central South Carolina — a value never documented in the recent years of real-time SPoRT-LIS output!  Most areas of SPoRT-LIS 0-2 m RSM exceeding ~60% correspond to areas of active minor to major river flooding across parts of southern Virginia and the Carolinas, as depicted in the USGS/NOAA river gauge network this morning (Fig. 6).

Finally, SPoRT is producing an experimental daily, real-time soil moisture percentile product based on a 33-year LIS-Noah county-by-county soil moisture climatology.  The soil moisture percentile map indicates where the current 0-2 m RSM soil moisture values lie in the present day’s historical soil moisture distribution for every county in the Continental U.S. The percentile product valid at 1200 UTC 27 September and 4 October is shown in Figure 7.  Primarily dry soil moisture values between ~5th-30th percentile are prevalent across the Carolinas on 27 September, corresponding reasonably well to the U.S. Drought Monitor moderate to severe drought areas from Figure 3. However, after the 10-20+ inches of rainfall over the past week, the 4 October soil moisture percentiles completely reversed across the region, with values > 98th percentile occurring in central South Carolina where the most severe flooding is taking place.  SPoRT plans to develop a brief training module on this percentile product prior to dissemination for display in AWIPS II at NWS WFOs, along with the current suite of SPoRT-LIS fields already available in AWIPS II.

Fig. 1. NWS River Forecast Center rainfall analysis for the week ending 1200 UTC 5 October 20125.

Fig. 1. NWS River Forecast Center rainfall analysis for the week ending 1200 UTC 5 October 2015.

Fig. 2. Comparison of NASA Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimate to the NWS/River Forecast Center analysis for the 24-hour period ending 1200 UTC 4 October 2015.

Fig. 2. Comparison of NASA Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimate to the NWS/River Forecast Center analysis for the 24-hour period ending 1200 UTC 4 October 2015.

Fig. 2. U.S. Drought Monitor weekly drought product valid 29 September 2015.

Fig. 3. U.S. Drought Monitor weekly drought product valid 29 September 2015.

Fig. 3. SPoRT-LIS total column (0-2 m) relative soil moisture valid on (left panel) 28 September, and (right panel) 5 October 2015.

Fig. 4. SPoRT-LIS total column (0-2 m) relative soil moisture valid on (left panel) 28 September, and (right panel) 5 October 2015.  Masked white areas represent water or urban pixels.

Fig. 4. One-week change in SPoRT-LIS total column relative soil moisture for the week ending 5 October 2015, as displayed in AWIPS II.

Fig. 5. One-week change in SPoRT-LIS total column relative soil moisture for the week ending 5 October 2015, as displayed in AWIPS II.  Maximum weekly change value > 58% is highlighted by the cursor position.  Masked black areas represent water or urban pixels.

Fig. 6. USGS and NWS River Forecast Center river gauge plot for the morning of 5 October 2015. River gauges experiencing flooding are indicated by the legend in the lower-right.

Fig. 6. USGS / NWS River Forecast Center river gauge plot for the morning of 5 October 2015. River gauges experiencing flooding are indicated by the legend in the lower-right.

Fig. 5. Experimental SPoRT-LIS total column relative soil moisture percentile product, valid at 1200 UTC on (left panel) 27 September, and (right panel) 4 October 2015.

Fig. 7. Experimental SPoRT-LIS total column relative soil moisture percentile product, valid at 1200 UTC on (left panel) 27 September, and (right panel) 4 October 2015.  Masked white areas represent water or urban pixels.

An Evaluation of the SFR Product during a mid-December Winter Storm for Northern New Mexico

The Albuquerque NWS recently began receiving an updated NESDIS snowfall rate (SFR) product from NASA SPoRT. We were anxious to see how the updated product performed during our most recent winter storm. A fast moving upper level trough and associated Pacific Front blasted into western New Mexico on the afternoon of Saturday, December 13. The upper low deepened and closed off over New Mexico with wrap around snow impacting northeast New Mexico through mid-day Sunday, December 14.  Ahead of the system, temperatures were very warm with Albuquerque reporting a high of 61 and Santa Fe reporting a high of 57 on Saturday.  The RGB snow-cloud product from 2045Z on Sunday depicts snow cover following the event. Four areas in the state were impacted – the western high terrain, the San Juan and Sangre de Cristo Mountains (mainly west slopes) in north central New Mexico, and extreme northeastern corner of New Mexico. Four yellow ovals mark areas to be discussed in this blog entry. Strong westerly, downslope flow on the backside of this storm system resulted in the snow-free region along the eastern slopes between Taos and Raton.
SnowCloud121414_2045Z

In the loop below, the 0.5 reflectivity mosaic and surface observations show the surface front moving into western New Mexico (left most oval in the snow-cloud product) during the period from 1942Z to 2318Z. In the first image, the winds have shifted to the northwest in Farmington (FMN) and rain is reported as temperatures are too warm to support snow. Note that throughout the loop the Farmington area, especially west and north of the site, there are no radar returns. The Four Corners area has poor to no radar coverage and it is an area where we hope the SFR product will help us. Snow was reported at Gallup (GUP) by 2030Z.

0.5 Reflectivity 1942Z to 2318Z

The SFR product was limited during this initial period, with only one swath covering New Mexico at 2034Z (shown below). This image (obtained from the SPoRT product page) shows that snow is detected in northeast Utah and northwest Colorado, but not in northwest New Mexico.  The Gallup area ended up with about one inch of snow while higher terrain south of Gallup reported two to three inches. While only rain was reported at the Farmington ASOS, the snow-cloud product shows some snow just to the east of Farmington where reports of one-half to an inch of snow was reported.

SPoRT_SFR_121314_2034Z

The next SFR product with coverage over New Mexico had a timestamp of 0338Z (14 December 2014), and is compared to the composite reflectivity image of 0336Z in the image below. Reflectivity is strongest just west and northwest of the Albuquerque ASOS (ABQ), which is reporting rain. The cold front however was moving quickly from west to east toward the ABQ metro area. The strong reflectivity returns to the northwest of Albuqurque are actually bright banding as rain began changing over to snow. The dual polarization hydrometeor classification algorithm showed the rain/snow line shifting quickly eastward. Fifteen minutes prior to this image, rain transition to snow was reported in Rio Rancho, just northwest of Albuquerque. The higher terrain just east of Albuquerque, the Sandia and Manzano Mountains, did receive snow accumulations of two to four inches and the SFR product highlights that area with light rates (blue) of about .02 inches/hour. The Santa Fe area (SAF) is not reporting snow at this time, but is highlighted with the max values of SFR, though snow reports in the Santa Fe area were generally less than 2 inches.  Recall that afternoon temperature were quite warm, making it difficult for snow to accumulate. The SFR product also depicts rates up to .05 inches/hour over the Sangre de Cristo mountains north and east of Santa Fe, where accumulations of 4 to 8 inches were reported. Interestingly, the SFR product is estimating precipitation around Santa Fe when the radar reflectivity pattern and observation do not indicate rain or snow. A portion of this area to the immediate northeast and east of ABQ is beam-blocked by the Sandia Mountains (yellow oval southwest of SAF).

mosaic_Comp_Ref_20141214_0336_SFR_0338Z

A similar comparison is shown for 13 hours later, or around 1655Z on December 15. (Another image was available around 08Z, but is not discussed in the post.)  Note that the SFR product depicts accumulating snow, albeit light, from eastern Taos through all but extreme southern Colfax County. Two stations (KAXX and KRTN) are reporting snow, but radar composite reflectivities do not extend over either location. Snow did accumulate at KAXX, but not at Raton (KRTN) where temperatures hovered right above freezing.

mosaic_Comp_Ref_20141214_1642_SFR_1645Z

Snow that is evident in extreme northeast New Mexico occurred after mainly 16Z and was associated with persistent wrap around precipitation (a SFR product was not available). The SFR product was not used in near real time for this event but was re-examined only a short time thereafter. However, the product did validate that we will indeed be able to complement radar void coverage areas in an operational forecast environment using polar-orbiting satellite imagery. This example will also serve to highlight potential product applications, advantages, and disadvantages for forecaster training prior to the upcoming NESDIS evaluation period.

Example of Limb Effects on NESDIS SFR Product

SPoRT continues to work with select NWS WFOs in evaluating the NESDIS SFR product.

One thing to take into consideration when using data from “whisk-broom” instruments on polar-orbiting satellites, such as the Advanced Microwave Sounding Unit (AMSU) used to generate the SFR product, is that data at the edge of the swath (i.e. limb) may provide misleading or erroneous observations. As the instrument scans farther from nadir, it is looking through more of the atmosphere, creating both a bigger observation field of view (i.e., larger pixel) and having the signal attenuated by more atmospheric constituents (e.g., in-cloud and falling snow).  As a result, when interpreting the SFR product, it is important to look for the extent of the swath (outlined in gray in the product in AWIPS) to determine whether the observed SFR is going to be limited by these limb effects.

Let’s take a look at an example over the NY Tri-State area for the post Super Bowl snow event.  In the first image, from Metop-A valid at 1458 UTC, there is a large area of snowfall across the area.  The heaviest SFRs appear to be around 1.2-1.5 in/hr (when multiplying the liquid equivalent by 10) across central and southern New Jersey.  However, an hour later (see second image from Metop-B valid at 1554 UTC), the shape of the heaviest SFR has expanded north and west and there are now readings over 2.0 +in/hr.  Other areas that had a SFR of less than 0.5 in/hr in the 1458 UTC image, appear to have a SFR of around 1.0 in/hr just an hour later, which is a large jump in intensity.

While it is certainly possible that the snow evolved and intensified in less than an hour, it is more likely that instrument limb effects are likely to blame for the larger SFRs in the second image.  Make sure to check that swath edge when using polar-orbiting satellite data!

NESDIS SFR Product from 1458 UTC on 3 February 2014 showing snow detected near nadir for Metop-A

NESDIS SFR Product from 1458 UTC on 3 February 2014 showing snow detected near nadir for Metop-A.

NESDIS SFR Product from 1554 UTC on 3 February 2014 depicting what are likely erroneous higher intensity SFR values along the swath edge from Metop-B.

NESDIS SFR Product from 1554 UTC on 3 February 2014 depicting what are likely erroneous higher intensity SFR values along the swath edge from Metop-B.