In S-DAV, both Tc and DAVc are spatially and temporally fixed [e.g., Ramage and Isacks, 2002; Tedesco, 2007]. Differently, in the D-DAV approach, the threshold DAVc is computed as DAVc = DAVJan. Feb. + 10 K, with DAVJan. Feb. being the January-February DAV average. The value of DAVc used in the S-DAV is 10 K [Ramage and Isacks, 2002]. We introduced the DAVJan. Feb offset because we observed that, for some pixels, DAV values during dry snow conditions could be as high as 12–14 K, with averaged January-February DAV values up to 6 K. The threshold Tc in D-DAV is computed modeling the bimodal distribution B describing the Tb histograms as B(p, m1, s1, m2, s2) = p· G(m1, s1) + (1 − p) · G(m2, s2), where p is the percentage of dry pixels, G is a Gaussian distribution and mi and si are, respectively, the mean and standard deviation of the ith normal distributions. For each pixel and year, the five parameters (p, m1, s1, m2, s2) are computed through a fitting procedure minimizing the mean square error between the values of the Tb histogram (January through August) and those of the bimodal distribution, using the Levenberg–Marquardt method. The optimal threshold value is then computed by minimizing the probability of erroneously classifying a dry pixel as a wet pixel and vice versa) as T = (−B ± (B2 − 4AC))/2A with A = s12 − s22, B = 2(m1 · s22 − m2 · s12) and C = m22 · s12 − m12 · s22 + 2 s12 · s22 · ln(s2 · p/s1 · (1 − p)) [Gonzalez and Wintz, 1987]. The value of T falling between m1 and m2 is the desired threshold Tc. For those cases when the fitting procedure does not converge, the threshold value on Tb is set to 255 K [Tedesco et al., 2006]. Once Tc and DAVc are computed, melting is identified when both of the following conditions are met: C1) DAVi ≥ DAVc and C2) TbiP ≥ Tbc., with P being either Ascending or Descending and i the day of the year. Melting is also assumed to occur when C3) (TbiAsc. ≥ TbThreshold and TbiDesc. ≥ TbThreshold), to account for melting that persist during nighttime [Tedesco, 2007]. The date of the end of the melt season is defined as the last day when DAVi ≥ DAVc and Tbi ≥ Tc.
 To evaluate the performance of the D-DAV algorithm at large spatial scales, we compared MODs and MEDs derived from QuikSCAT [Wang et al., 2008] with those derived from D-DAV. Figure 1 shows the mean (2000 – 2006) MODs (Figure 1a) and MEDs (Figure 1b) derived with D-DAV and the difference between MODs derived from QuikSCAT and SSM/I using D-DAV (Figure 1c) and between the melt off dates from QuikSCAT and MEDs derived from D-DAV (Figure 1d), for the period 2000 – 2006. Histograms of the differences between MODs and MEDs obtained with QuikSCAT and D-DAV are also reported in Figures 1e and 1f. The values of the mean and standard deviation of the normal distributions fitting the two histograms are, respectively, 1.74 days and 1.12 days in the case of the MOD and −1.67 days and 2.93 days in the case of the MED. No evident spatial pattern related to features such as vegetation and elevation is observed. D-DAV detects MOD (MED) earlier (later) than QuikSCAT. The difference between the two algorithms might be due to the different cell grid size of the two data sets, to the different frequencies, and to the adopted threshold values.
 We also compared the outputs of the D-DAV algorithm with estimates of MODs and MEDs derived from the analysis of daily snow depth (SD) and SAT measured by 49 stations of the World Meteorological Organization (WMO) for three snow seasons (2003 through 2006, http://www.ncdc.noaa.gov). The stations used in this study and the related data set were previously used by Tedesco and Miller , where they are fully described. Since we lack the required information to solve the surface energy balance equations and compute the snow temperature, we assumed that melting occurs when SAT is exceeding 0 °C. The correlation between MODs from D-DAV and those estimated from Ts for all stations and years is R2 = 0.8 with a mean absolute error of 4.8 days and a standard deviation of 8.6 days. During our analysis, we noted that D-DAV can indicate melting when SAT is still below but close to 0 °C. Possible explanations include different spatial scales at which the two data sets are acquired, the presence of subsurface melting (not identified by SAT analysis) and the fact that WMO stations report the 2m air temperature rather than the actual snow temperature. Following Wang et al. , we also studied the histograms of SAT one and two days before and on the same day when melting is identified by D-DAV. The number of SAT occurrences above 0 ° C was 10% two days before the identified melting date, 29% one day before and 65% on the date when melting is estimated by the D-DAV algorithm. The shift in the staistical distribution of SAT from largely below freezing to above freezing during the estimated D-DAV melt date confirms that the D-DAV algorithm is sensitive to the melt onset signal. When comparing the date of the last day of melting from D-DAV and the date of snow disappearance from snow depth measurements, we found a correlation of R2 = 0.4, with a mean error (over all samples) of 14 days and a standard deviation of 12 days. We point out here that comparing the D-DAV MEDs with the snow disappearance dates from WMO data has intrinsic problems. As pointed out by Wang et al. , WMO stations do not report a ‘zero’ snow depth for days when there is no snow but rather a flag value (9999), also corresponding to missing data. Moreover, the MED computed by the D-DAV might differ from the actual date of snow disappearance.